@inproceedings{li-etal-2025-multilingpot,
title = "{M}ulti{L}ing{P}o{T}: Boosting Mathematical Reasoning in {LLM}s through Multilingual Program Integration",
author = "Li, Nianqi and
Liang, Zujie and
Yuan, Siyu and
Liang, Jiaqing and
Wei, Feng and
Xiao, Yanghua",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1079/",
doi = "10.18653/v1/2025.findings-emnlp.1079",
pages = "19794--19811",
ISBN = "979-8-89176-335-7",
abstract = "Program-of-Thought, which aims to use program instead of natural language 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 research only focuses on single language PoT, ignoring the differences between programming languages. Therefore, this paper proposes a multilingual programme reasoning method, MultiLingPoT, and deeply explores the impact of multilingual integration in the training and inference. This method allows the model to answer questions using multiple languages by fine-tuning on multilingual data and improving individual language{'}s reasoning accuracy by 2.5{\%}. Additionally, prior and posterior selection methods are used to help the model select the most suitable language during inference, and achieves 8{\%} performance gains. Finally, our code metric analysis shows that language differences manifest in encapsulation levels and implementation granularity, while strategic deviation from language conventions can enhances code performance."
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%0 Conference Proceedings
%T MultiLingPoT: Boosting Mathematical Reasoning in LLMs through Multilingual Program Integration
%A Li, Nianqi
%A Liang, Zujie
%A Yuan, Siyu
%A Liang, Jiaqing
%A Wei, Feng
%A Xiao, Yanghua
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F li-etal-2025-multilingpot
%X Program-of-Thought, which aims to use program instead of natural language 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 research only focuses on single language PoT, ignoring the differences between programming languages. Therefore, this paper proposes a multilingual programme reasoning method, MultiLingPoT, and deeply explores the impact of multilingual integration in the training and inference. This method allows the model to answer questions using multiple languages by fine-tuning on multilingual data and improving individual language’s reasoning accuracy by 2.5%. Additionally, prior and posterior selection methods are used to help the model select the most suitable language during inference, and achieves 8% performance gains. Finally, our code metric analysis shows that language differences manifest in encapsulation levels and implementation granularity, while strategic deviation from language conventions can enhances code performance.
%R 10.18653/v1/2025.findings-emnlp.1079
%U https://aclanthology.org/2025.findings-emnlp.1079/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1079
%P 19794-19811
Markdown (Informal)
[MultiLingPoT: Boosting Mathematical Reasoning in LLMs through Multilingual Program Integration](https://aclanthology.org/2025.findings-emnlp.1079/) (Li et al., Findings 2025)
ACL