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README.md
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# ReaRAG-9B
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<p align="center">
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🤗 <a href="https://huggingface.co/datasets/THU-KEG/ReaRAG-20k" target="_blank">Dataset</a> • 💻 <a href="https://github.com/THU-KEG/ReaRAG" target="_blank">GitHub</a> • 📃 <a href="https://arxiv.org/abs/2503.21729" target="_blank">Paper</a>
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</p>
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ReaRAG-9B is trained based on glm-4-9b, with enhanced capability to generate knowledge-guided reasoning chains for iterative RAG. The model supports a context window of up to 8k tokens.
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Please refer to the [Inference](https://github.com/THU-KEG/ReaRAG?tab=readme-ov-file#%EF%B8%8F-inference) section in the GitHub repository for usage detail.
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# 📚 Citation
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If you use this dataset in your research or projects, please consider citing our work:
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```
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@article{lee2025rearag,
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title={ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation},
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author={Lee, Zhicheng and Cao, Shulin and Liu, Jinxin and Zhang, Jiajie and Liu, Weichuan and Che, Xiaoyin and Hou, Lei and Li, Juanzi},
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journal={arXiv preprint arXiv:2503.21729},
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year={2025}
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}
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```
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