Summary of Multi-tool Integration Application For Math Reasoning Using Large Language Model, by Zhihua Duan et al.
Multi-tool Integration Application for Math Reasoning Using Large Language Model
by Zhihua Duan, Jialin Wang
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel multi-tool application framework for mathematical reasoning leverages large language models (LLMs) and multiple external tools to achieve more comprehensive and accurate mathematical reasoning. The framework consists of three main components: the Math Tool, which performs basic mathematical calculations through interaction with LLM; the Code Tool, which generates code fragments that comply with syntax rules and executes them to support complex mathematical problems; and the CoT Tool, which enhances logical coherence and accuracy through iterative reasoning. Additionally, self-consistency tools select the final answer based on different parameters, improving consistency and reliability of reasoning. Experimental results show significant performance improvements in mathematical reasoning tasks, with an accuracy of 89.09% in Task 4 compared to GPT3+FewShot baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to improve math skills using computers. It combines the power of big language models and special tools to do more complex math problems. The system has three parts: one that does simple math, one that writes code for more complicated problems, and another that checks the answers and makes sure they’re correct. This combination helps solve math problems better than other systems. Tests show it’s really good at math, with an accuracy of 89.09%. |
Keywords
» Artificial intelligence » Syntax
