Papers by Guoqiang Zhong

2 papers
MuMath: Multi-perspective Data Augmentation for Mathematical Reasoning in Large Language Models (2024.findings-naacl)

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Challenge: Large Language Models (LLMs) that integrate with external Python interpreters are not able to demonstrate the calculation process, which compromises user-friendliness and understanding of problem-solving steps.
Approach: They propose to use LLaMA-2 to refine LLti-perspective augmentation methods to improve performance.
Outcome: The proposed model achieves 88.3% on GSM8K and 34.5% on MATH.
MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning (2024.emnlp-main)

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Challenge: a method to combine the advantages of open-source and tool-free LLMs remains to be explored.
Approach: They propose a method to integrate open-source LLMs with external Python interpreters and augment math reasoning data.
Outcome: The proposed method improves on GSM8K and MATH with the use of external tools.

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