Create README.md

README.md

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+
+# Rapidata Generated Joke Preference Dataset
+
+<a href="https://www.rapidata.ai">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/66f5624c42b853e73e0738eb/jfxR79bOztqaC6_yNNnGU.jpeg" width="400" alt="Dataset visualization">
+</a>
+
+We collected 1'000'000+ human opinions on the jokes generated by state-of-the-art LLMs. It took us ~125 hours to get all of the responses.
+The jokes are evenly distributed across 5 languages: English, Arabic, Japanese, Vietnamese, Portuguese and across 4 model configurations.
+
+## Joke Generation Process
+
+We prompt each model in a certain language to tell a joke on a given topic. Examples include bicycle, ghost, elephant. 
+We want to see which jokes a model can generate on each topic, so for each topic we try to generate 5 different jokes. If the model is unable to generate a different joke for 10 times, we stop the generation process.
+
+So, if all of the combinations resulted in 5 unique jokes, we would have:
+\\(5 (\mathrm{languages}) \times 4 (\mathrm{models}) \times 100 (\mathrm{topics}) \times 5 (\mathrm{samples}) = 10\textrm{'}000\\) jokes.
+
+However, there are less, only \\(9\textrm{'}978\\) jokes in the dataset, because sometimes models failed to produce 5 different jokes on a given topic in English. For other languages, all of the jokes were generated.
+
+
+## Columns
+- `model` — model that was used for joke generation, one of
+  - `gpt-4o`
+  - `claude-sonnet-4-20250514`
+  - `gemini-2.5-flash` (no thinking tokens)
+  - `gemini-2.5-flash-thinking` (default number of thinking tokens)
+- `joke_english` — the `joke` translated into English using GPT-4o
+- `yes_ratios` — fraction of "Yes" responses
+- `yes_user_score_ratios` — fraction of user_scores of people (internal metric which indicates trust in a user) who responded "Yes"
+
+<!-- Or, more formally,
+`yes_user_score_ratios` = \\(\frac{\sum_{r \in R}{\mathbb{1}\{r_{\mathrm{ans}} = \text{"Yes"}\} \cdot r_{\mathrm{user\_score}}}}{\sum_{r \in R}{r_{\mathrm{user\_score}}}}\\) -->
+
+
+## Analysis Notebook
+We prepared a [notebook]() with a simple data analysis of our results. Here are the key graphs from it.
+
+<center>
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/67054b6963cd3c1d5da67fc5/iryJQZQKqnSEGR4iLqS_d.png" alt="drawing" width="450"/>
+    <figcaption>Model scores. The non-thinking version of Gemini has the lowest score while all other thinking model configurations perform similarly</figcaption>
+</center>
+
+<center>
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/67054b6963cd3c1d5da67fc5/uNIJO_CrH23YAHHkfTKNl.png" alt="drawing" width="450"/>
+  <figcaption>Language scores. We observe a significant difference in average scores for different languages. Quite unsurprisingly, the language with the highest quality jokes is English</figcaption>
+</center>
+
+To have a better feel for the types of jokes models tend to produce in different languages, we made an interactive plot of joke embeddings.
+We project embeddings of `joke_english` on a 2D space and draw them on a canvas.
+
+<figure>
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/67054b6963cd3c1d5da67fc5/cxmKoOZEyBMs0JuLc_6iG.png" alt="A Japanese joke">
+  <figcaption>Projected joke embeddings on an interactive canvas. Hovering on an example of a top 1% Japanese joke. The success of this joke shows the style of jokes preferred by the Japanese</figcaption>
+</figure>
+
+For all of the graphs, check out the [notebook]().
+
+These are just the first steps in exploring the quality of generated humour and its reception by different cultures. We can't wait to see what you can do with this dataset!
+
+
+## About Rapidata
+Rapidata's technology makes collecting human feedback at scale faster and more accessible than ever before. Visit [rapidata.ai](https://www.rapidata.ai/) to learn more about how we're revolutionizing human feedback collection for AI development.