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arXiv · 2502.12589

RM-PoT: Reformulating Mathematical Problems and Solving via Program of Thoughts

Yu Zhang·Shujun Peng·Nengwu Wu·Xinhan Lin·Yang Hu·Jie Tang
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Abstract

Recently, substantial advancements have been made in training language models to carry out step-by-step reasoning for solving intricate numerical reasoning tasks. Beyond the methods used to solve these problems, the structure and formulation of the problems themselves also play a crucial role in determining the performance of large language models. We observe that even small changes in the surface form of mathematical problems can have a profound impact on both the answer distribution and solve rate. This highlights the vulnerability of LLMs to surface-level variations, revealing its limited robustness when reasoning through complex problems. In this paper, we propose RM-PoT, a three-stage framework that integrates problem reformulation (RM), code-aided reasoning (PoT), and domain-aware few-shot learning to address these limitations. Our approach first reformulates the input problem into diverse surface forms to reduce structural bias, then retrieves five semantically aligned examples from a pre-constructed domain-specific question bank to provide contextual guidance, and finally generates executable Python code for precise computation.

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Yu Zhang, Shujun Peng, Nengwu Wu, Xinhan Lin, Yang Hu, Jie Tang. 2025-02-18. RM-PoT: Reformulating Mathematical Problems and Solving via Program of Thoughts. https://arxiv.org/abs/2502.12589

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