|
|
--- |
|
|
datasets: |
|
|
- THU-KEG/ReaRAG-20k |
|
|
language: |
|
|
- en |
|
|
base_model: |
|
|
- THUDM/glm-4-9b |
|
|
pipeline_tag: question-answering |
|
|
tags: |
|
|
- rag |
|
|
- reasoning |
|
|
--- |
|
|
# ReaRAG-9B |
|
|
<p align="center"> |
|
|
🤗 <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> |
|
|
</p> |
|
|
|
|
|
ReaRAG-9B is trained based on [glm-4-9b](https://huggingface.co/THUDM/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. |
|
|
|
|
|
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. |
|
|
|
|
|
# 📚 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} |
|
|
} |
|
|
``` |
