THU-KEG/ReaRAG-20k

Dataset

1

stars

6

commits

1

linked in READMEs

Apr 18, 2025

updated

rag
reasoning

README

📘 Dataset Card for ReaRAG-20k

🤗 Model • 💻 GitHub • 📃 Paper

ReaRAG-20k is a reasoning-focused dataset designed for training the ReaRAG model. It contains approximately 20,000 multi-turn retrieval examples constructed from the QA datasets such as HotpotQA, MuSiQue, and Natural Questions (NQ).

Each instance follows a conversational format supporting reasoning and retrieval steps:

{
    "messages": [{"role": "user", "content": "..."}, 
                 {"role": "assistant", "reasoning": "..."},
                 {"role": "observation", "content": "..."}, ...]
}

During sft, the loss is computed only on messages that contain the reasoning key, rather than the content key.

🔗 Resources

📚 Citation

If you use this dataset in your research or projects, please consider citing our work:

@article{lee2025rearag,
  title={ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation},
  author={Lee, Zhicheng and Cao, Shulin and Liu, Jinxin and Zhang, Jiajie and Liu, Weichuan and Che, Xiaoyin and Hou, Lei and Li, Juanzi},
  journal={arXiv preprint arXiv:2503.21729},
  year={2025}
}

Contributors

ZhiCheng0326

6 commits

THU-KEG/ReaRAG-20k

Dataset

1

stars

6

commits

1

linked in READMEs

Apr 18, 2025

updated

rag
reasoning

README

📘 Dataset Card for ReaRAG-20k

🤗 Model • 💻 GitHub • 📃 Paper

ReaRAG-20k is a reasoning-focused dataset designed for training the ReaRAG model. It contains approximately 20,000 multi-turn retrieval examples constructed from the QA datasets such as HotpotQA, MuSiQue, and Natural Questions (NQ).

Each instance follows a conversational format supporting reasoning and retrieval steps:

{
    "messages": [{"role": "user", "content": "..."}, 
                 {"role": "assistant", "reasoning": "..."},
                 {"role": "observation", "content": "..."}, ...]
}

During sft, the loss is computed only on messages that contain the reasoning key, rather than the content key.

🔗 Resources

📚 Citation

If you use this dataset in your research or projects, please consider citing our work:

@article{lee2025rearag,
  title={ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation},
  author={Lee, Zhicheng and Cao, Shulin and Liu, Jinxin and Zhang, Jiajie and Liu, Weichuan and Che, Xiaoyin and Hou, Lei and Li, Juanzi},
  journal={arXiv preprint arXiv:2503.21729},
  year={2025}
}

Contributors

ZhiCheng0326

6 commits