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About this paper
Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng and 2 more
MathChat introduces a conversational agent framework that enables Large Language Models to tackle challenging mathematical problems by integrating external tools with iterative feedback. The system attained 44.71% accuracy on Level 5 problems from the MATH dataset, surpassing previous tool-using approaches by 6%.
Key insights
- Integrating external computational tools within a dynamic, feedback-driven conversational agent framework significantly enhances an LLM's ability to solve complex mathematical problems.
- The iterative nature of conversations, managed by a user proxy agent, is crucial for mitigating execution errors and refining reasoning steps by providing real-time feedback and correction opportunities.
- The MathChat framework demonstrates flexibility, allowing for the integration of various prompts and computational tools, which supports its extensibility to new LLMs and problem domains.
Summary by alphaXiv.
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