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

• URL: http://arxiv.org/abs/2502.12589v1
• Date: Tue, 18 Feb 2025 06:54:32 GMT
• Title: RM-PoT: Reformulating Mathematical Problems and Solving via Program of Thoughts
• Authors: Yu Zhang, Shujun Peng, Nengwu Wu, Xinhan Lin, Yang Hu, Jie Tang,
• Abstract summary: We propose a three-stage framework that integrates problem reformulation (RM), code-aided reasoning (PoT) and domain-aware few-shot learning. Our approach first reformulates the input problem into diverse surface forms to reduce structural bias, then retrieves five semantically aligned examples to provide contextual guidance.
• Score: 13.07180561863778
• License:
• 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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