ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline
<|reference_start|>ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline: Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving. While many strategies and datasets to enhance LLMs' mathematics are developed, it remains a challenge to simultaneously maintain and improve both language and mathematical capabilities in deployed LLM systems.In this work, we tailor the Self-Critique pipeline, which addresses the challenge in the feedback learning stage of LLM alignment. We first train a general Math-Critique model from the LLM itself to provide feedback signals. Then, we sequentially employ rejective fine-tuning and direct preference optimization over the LLM's own generations for data collection. Based on ChatGLM3-32B, we conduct a series of experiments on both academic and our newly created challenging dataset, MathUserEval. Results show that our pipeline significantly enhances the LLM's mathematical problem-solving while still improving its language ability, outperforming LLMs that could be two times larger. Related techniques have been deployed to ChatGLM\footnote{\url{https://chatglm.cn}}, an online serving LLM. Related evaluation dataset and scripts are released at \url{https://github.com/THUDM/ChatGLM-Math}.<|reference_end|>
@article{xu2024chatglm-math:,
    title={ChatGLM-Math: Improving Math Problem-Solving in Large Language Models
  with a Self-Critique Pipeline},
    author={Yifan Xu, Xiao Liu, Xinghan Liu, Zhenyu Hou, Yueyan Li, Xiaohan Zhang,
  Zihan Wang, Aohan Zeng, Zhengxiao Du, Wenyi Zhao, Jie Tang, Yuxiao Dong},
    journal={arXiv preprint arXiv:2404.02893},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02893},
    primaryClass={cs.CL}
}