[2405.12100] DOP: Diagnostic-Oriented Prompting for Large Language Models in Mathematical Correction
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arXiv:2405.12100 (cs)
[Submitted on 20 May 2024]

Title:DOP: Diagnostic-Oriented Prompting for Large Language Models in Mathematical Correction

Authors:Hao Chen, Biaojie Zeng, Xin Lin, Liang He, Aimin Zhou
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Abstract: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 this https URL.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2405.12100 [cs.CL]
  (or arXiv:2405.12100v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2405.12100

Submission history

From: Hao Chen [view email]
[v1] Mon, 20 May 2024 15:13:22 UTC (8,495 KB)
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