| --- |
| license: mit |
| task_categories: |
| - conversational |
| - text-generation |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| pretty_name: UltraChat |
| --- |
| |
| # Dataset Card for Dataset Name |
|
|
| ## Dataset Description |
|
|
| An open-source, large-scale, and multi-round dialogue data powered by Turbo APIs. In consideration of factors such as safeguarding privacy, **we do not directly use any data available on the Internet as prompts**. |
| To ensure generation quality, two separate ChatGPT Turbo APIs are adopted in generation, where one plays the role of the user to generate queries and the other generates the response. |
| We instruct the user model with carefully designed prompts to mimic human user behavior and call the two APIs iteratively. The generated dialogues undergo further post-processing and filtering. |
| ULtraChat is composed of three sectors: |
|
|
| - 🌏 **Questions about the World**: The dialogue data in this sector is derived from a wide range of inquiries related to concepts, entities, and objects from the real world. The topics covered are extensive, spanning areas such as technology, art, and entrepreneurship. |
| - ✍🏻 **Writing and Creation**: The dialogue data in this sector is driven by the demands for writing/creation from scratch, and encompasses any tasks that an AI assistant may aid within the creative process, spanning from email composition to crafting narratives and plays, and beyond. |
| - 📋 **Assistance on Existent Materials**: The dialogue data in this sector is generated based on existing materials, including but not limited to rewriting, continuation, summarization, and inference, covering a diverse range of topics. |
|
|
| - Repository: [UltraChat](https://github.com/thunlp/UltraChat) |
| - Explorer: [plain-explorer](http://39.101.77.220/), [Nomic-AI-Atlas-Explorer](https://atlas.nomic.ai/map/0ce65783-c3a9-40b5-895d-384933f50081/a7b46301-022f-45d8-bbf4-98107eabdbac) |
|
|
| ## Dataset Structure |
|
|
| Each line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line. |
|
|
| ``` |
| { |
| "id": "0", |
| "data": [ |
| "How can cross training benefit groups like runners, swimmers, or weightlifters?", |
| "Cross training can benefit groups like runners, swimmers, or weightlifters in the following ways: ...", |
| "That makes sense. I've been wanting to improve my running time, but I never thought about incorporating strength training. Do you have any recommendations for specific exercises?", |
| "Sure, here are some strength training exercises that can benefit runners: ...", |
| "Hmm, I'm not really a fan of weightlifting though. Can I incorporate other forms of exercise into my routine to improve my running time?", |
| "Yes, absolutely! ...", |
| "..." |
| ] |
| } |
| ``` |
|
|
|
|
| ### Citation Information |
|
|
| ```bibtex |
| @article{ding2023enhancing, |
| title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, |
| author={Ding, Ning and Chen, Yulin and Xu, Bokai and Qin, Yujia and Zheng, Zhi and Hu, Shengding and Liu, Zhiyuan and Sun, Maosong and Zhou, Bowen}, |
| journal={arXiv preprint arXiv:2305.14233}, |
| year={2023} |
| } |
| ``` |
