Computer Science > Computation and Language
arXiv:2412.13791 (cs)
[Submitted on 18 Dec 2024]
Title:Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models
Authors:Xinyu Pang,Ruixin Hong,Zhanke Zhou,Fangrui Lv,Xinwei Yang,Zhilong Liang,Bo Han,Changshui Zhang
View a PDF of the paper titled Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models, by Xinyu Pang and 7 other authors
View PDFHTML (experimental)Abstract: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%.
| Comments: | COLING 2025 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2412.13791 [cs.CL] |
| (orarXiv:2412.13791v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2412.13791 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models, by Xinyu Pang and 7 other authors
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