Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models (2412.13791v1)

Abstract: Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing 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%.

• The paper introduces a novel framework that augments LLMs with domain-specific checklists and formula retrieval to improve physics problem-solving.
• The framework employs a structured approach with stages for problem analysis, formula retrieval, and guided reasoning to ensure accuracy.
• Empirical results on SciBench show a 5.8% accuracy boost, underscoring the framework's practical benefits in tackling physics challenges.

Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with LLMs

LLMs have been increasingly explored for their potential in complex reasoning tasks, including solving physics problems. Despite their advancements, these models often face challenges pertaining to insufficient knowledge and the incorrect application of existing knowledge. The paper "Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with LLMs" addresses these crucial gaps by introducing a novel framework called Physics Reasoner. This framework integrates domain-specific knowledge acquisition and application processes to enhance LLMs' ability to solve challenging physics problems.

Summary of the Framework

The proposed Physics Reasoner framework is a structured approach consisting of three primary stages: problem analysis, formula retrieval, and guided reasoning. It leverages a comprehensive physics-specific formula set and employs detailed checklist methodologies to improve both the acquisition of relevant physics knowledge and its application.

1. Problem Analysis: This initial stage involves breaking down a physics problem to extract known variables and translate the problem into a structured format that LLMs can comprehend. The use of checklists in this stage ensures accurate extraction and initialization of variables.
2. Formula Retrieval: The framework uses a curated set of physics formulae categorized into specific domains. By utilizing this formula set, the system identifies relevant equations that can be applied to solve the problem at hand.
3. Guided Reasoning: This final phase employs guidance via checklists to ensure that the application of the retrieved formulae is explicit and correct. This stage also involves refining and verifying the reasoning process to minimize errors in the final solution.

Empirical Results

The paper demonstrates the efficacy of Physics Reasoner through comprehensive experiments on the SciBench benchmark datasets, which include diverse physics topics such as electronics and thermodynamics. Notably, the framework achieved a significant average accuracy improvement of 5.8% over existing methods.

Implications and Future Directions

Physics Reasoner shows considerable promise for improving the reasoning capabilities of LLMs in physics, pointing towards a more generalizable approach for other STEM fields requiring domain-specific knowledge integration. The use of exhaustive formula sets and checklists highlights the importance of augmenting LLMs with structured external knowledge.

Future advancements could include expanding the scope of the formula set to cover more intricate, interdisciplinary problems, thereby enhancing the reasoning capabilities further. Additionally, integrating autonomous feedback mechanisms within the framework could drive further improvements in model self-correction and learning.

In summary, the Physics Reasoner framework represents a significant methodological advancement in the application of LLMs to physics problems, demonstrating enhanced accuracy and reliability through systematic knowledge augmentation and guided reasoning strategies. This contribution not only advances the state-of-the-art in LLM-based physics reasoning but also offers insights into tackling similar challenges across other domain-specific applications in AI.