MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical
  Reasoning
Ke Wang Houxing Ren Aojun Zhou Zimu Lu Sichun Luo Weikang Shi Renrui Zhang Linqi Song Mingjie Zhan Hongsheng Li
The recently released GPT-4 Code Interpreter has demonstrated remarkable
proficiency in solving challenging math problems, primarily attributed to its
ability to seamlessly reason with natural language, generate code, execute
code, and continue reasoning based on the execution output. In this paper, we
present a method to fine-tune open-source language models, enabling them to use
code for modeling and deriving math equations and, consequently, enhancing
their mathematical reasoning abilities. We propose a method of generating novel
and high-quality datasets with math problems and their code-based solutions,
referred to as MathCodeInstruct. Each solution interleaves natural language,
code, and execution results. We also introduce a customized supervised
fine-tuning and inference approach. This approach yields the MathCoder models,
a family of models capable of generating code-based solutions for solving
challenging math problems. Impressively, the MathCoder models achieve
state-of-the-art scores among open-source LLMs on the MATH (45.2%) and GSM8K
(83.9%) datasets, substantially outperforming other open-source alternatives.
Notably, the MathCoder model not only surpasses ChatGPT-3.5 and PaLM-2 on GSM8K
and MATH but also outperforms GPT-4 on the competition-level MATH dataset. The
dataset and models will be released at https://github.com/mathllm/MathCoder.