"Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models"

LLMs solve physics problems by combining formula databases with guided reasoning checklists.

Physics Reasoner enhances LLMs' physics problem-solving by combining a comprehensive formula database with guided reasoning checklists, achieving state-of-the-art accuracy improvements.

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https://arxiv.org/abs/2412.13791

Original Problem 🤔:

→ LLMs struggle with physics problems due to insufficient knowledge and incorrect application of concepts

→ Current methods achieve only 6.8% accuracy on SciBench physics problems

→ Existing approaches lack specific mechanisms to address knowledge deficiency

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Solution in this Paper 🔧:

→ Introduces Physics Reasoner, a three-stage framework combining formula knowledge and guided reasoning

→ Constructs a formula set containing 122 physics formulae across 36 subfields

→ Implements problem analysis stage to extract variables and convert to Python code

→ Uses formula retrieval to identify relevant equations from the database

→ Applies guided reasoning with checklists to verify calculations and knowledge application

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Key Insights 💡:

→ LLMs need explicit physics knowledge augmentation for better reasoning

→ Structured checklists significantly improve knowledge application accuracy

→ Python code integration ensures precise calculations

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Results 📊:

→ Achieves 5.8% average accuracy improvement on SciBench

→ Reduces comprehension errors and knowledge misapplication

→ Shows consistent improvement across GPT-3.5, GPT-4, and Llama3 models