Math world problems correction(MWPC) is a novel task dedicated to rectifying reasoning errors in the process of solving mathematical problems. In this paper, leveraging the advancements in large language models (LLMs), we address two key objectives:(1) Distinguishing between mathematical reasoning and error correction; (2) Exploring strategies to enhance the error correction capabilities of LLMs in mathematics to solve MWPC task. We noticed that, in real-time education,assisting students in recognizing their mistakes is more crucial than simply providing correct answers. However, current research tends to prioritize obtaining accurate solutions to math problems rather than correcting potentially incorrect ones. Therefore, we modify the research paradigm, demonstrating that improving mathematical reasoning abilities does not equate to mastery in error correction. Meanwhile, we propose a novel method called diagnostic-oriented promping(DOP) aimed at facilitating LLMs to excel in error correction. In experiments, DOP has shown outstanding performance, highlighting its significant impact. We argue that in mathematical education, the demand for outstanding correctors surpasses that for proficient reasoners. Codes and data are available on https://github.com/ChenhaoEcnuCS/Reason-Correct.

翻译：数学应用题纠错是一项致力于修正数学问题求解过程中推理错误的新任务。本文借助大语言模型的发展，聚焦两个核心目标：（1）区分数学推理与错误修正；（2）探索提升大语言模型在数学领域纠错能力的策略以解决MWPC任务。我们注意到，在实时教育场景中，帮助学生识别其错误比单纯提供正确答案更为关键。然而，当前研究更倾向优先获取数学问题的正确解法，而非修正可能存在的错误答案。为此，我们调整研究范式，证明提升数学推理能力并不等同于掌握纠错技能。同时，我们提出名为“面向诊断的提示方法”的新方法，旨在促进大语言模型在纠错任务中表现卓越。实验表明，DOP展现出显著性能优势，凸显其重要价值。我们认为，在数学教育中，对优秀纠错者的需求远超对熟练推理者的需求。代码与数据已开源至https://github.com/ChenhaoEcnuCS/Reason-Correct。