MultiLingPoT：通过多语言程序微调增强数学推理
MultiLingPoT: Enhancing Mathematical Reasoning with Multilingual Program Fine-tuning
AI总结：
本文提出MultiLingPoT，一种通过多语言程序微调增强大语言模型数学推理的方法，利用先验与后验混合选择最佳编程语言，实验显示性能提升约2.5%，混合后可达6%。
摘要：
程序思维（PoT）旨在使用编程语言而非自然语言作为推理的中间步骤，是大型语言模型解决数学问题的重要方式。由于不同的编程语言在不同领域各有所长，自然应针对特定问题选用最合适的语言。然而，当前的PoT研究仅关注单语言PoT，忽略了不同编程语言之间的差异。因此，本文提出了一种多语言程序推理方法MultiLingPoT。该方法通过在多语言数据上进行微调，使模型能够使用多种编程语言回答问题。此外，采用先验与后验混合方法帮助模型为每个问题选择最合适的语言。实验结果表明，MultiLingPoT的训练使每种编程语言的数学推理能力提升了约2.5%。而且，通过适当的混合，MultiLingPoT的性能可进一步提升，与采用数据增强的单语言PoT相比，实现了6%的提升。本文的资源可在https://github.com/Nianqi-Li/MultiLingPoT获取。
英文摘要：
Program-of-Thought (PoT), which aims to use programming language instead of natural language as an intermediate step in reasoning, is an important way for LLMs to solve mathematical problems. Since different programming languages excel in different areas, it is natural to use the most suitable language for solving specific problems. However, current PoT research only focuses on single language PoT, ignoring the differences between different programming languages. Therefore, this paper proposes an multilingual program reasoning method, MultiLingPoT. This method allows the model to answer questions using multiple programming languages by fine-tuning on multilingual data. Additionally, prior and posterior hybrid methods are used to help the model select the most suitable language for each problem. Our experimental results show that the training of MultiLingPoT improves each program's mathematical reasoning by about 2.5\%. Moreover, with proper mixing, the performance of MultiLingPoT can be further improved, achieving a 6\% increase compared to the single-language PoT with the data augmentation.Resources of this paper can be found at https://github.com/Nianqi-Li/MultiLingPoT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
