Summary of Mumath-code: Combining Tool-use Large Language Models with Multi-perspective Data Augmentation For Mathematical Reasoning, by Shuo Yin et al.
MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning
by Shuo Yin, Weihao You, Zhilong Ji, Guoqiang Zhong, Jinfeng Bai
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel approach to enhance mathematical reasoning capabilities in Large Language Models (LLMs). By integrating new math questions via multi-perspective data augmenting methods and synthesizing code-nested solutions, the authors develop MuMath-Code, an open-source LLM that leverages the advantages of both external tools and data augmentation. The proposed two-stage training strategy is designed to fully leverage the augmented data, resulting in a state-of-the-art performance on GSM8K and MATH benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to solve math problems better. They combine two existing ideas: using a tool (like Python) and adding more math questions to train the computer. The result is an open-source program called MuMath-Code that can solve math problems very well. This is important because it shows how combining different approaches can lead to even better results. |
Keywords
» Artificial intelligence » Data augmentation
