Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing large language models (LLMs) frequently fail due to a lack of knowledge or incorrect knowledge application. To mitigate these issues, we propose Physics Reasoner, a knowledge-augmented framework to solve physics problems with LLMs. Specifically, the proposed framework constructs a comprehensive formula set to provide explicit physics knowledge and utilizes checklists containing detailed instructions to guide effective knowledge application. Namely, given a physics problem, Physics Reasoner solves it through three stages: problem analysis, formula retrieval, and guided reasoning. During the process, checklists are employed to enhance LLMs' self-improvement in the analysis and reasoning stages. Empirically, Physics Reasoner mitigates the issues of insufficient knowledge and incorrect application, achieving state-of-the-art performance on SciBench with an average accuracy improvement of 5.8%.
@article{arxiv.2412.13791,
title = {Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models},
author = {Xinyu Pang and Ruixin Hong and Zhanke Zhou and Fangrui Lv and Xinwei Yang and Zhilong Liang and Bo Han and Changshui Zhang},
journal= {arXiv preprint arXiv:2412.13791},
year = {2024}
}