Improving Mathematical Reasoning Capabilities in Large Language Models via Reasoning Process Error Classification
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Computer Science > Computation and Language
arXiv:2609.15145 (cs)
[Submitted on 14 Sep 2026]
Title:Improving Mathematical Reasoning Capabilities in Large Language Models via Reasoning Process Error Classification
View a PDF of the paper titled Improving Mathematical Reasoning Capabilities in Large Language Models via Reasoning Process Error Classification, by Runa Yoshida and 2 other authors
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Abstract:The reasoning ability of large language models (LLMs) is a critical factor for practical LLM-based applications. To investigate the current reasoning capability of LLMs, we clarify the types of errors that arise in LLMs' reasoning processes on mathematical datasets. We focus on problems where LLMs produce an incorrect answer. We define errors in the reasoning process as reasoning errors and manually analyze the features of reasoning errors. We defined and classified 21 error classes and identified the frequently occurring classes among them. Beyond qualitative evaluation, we leverage the evaluation results to improve the reasoning capability. We designed a prompt that explicitly focuses on eight error classes. The experiments demonstrate that this prompt effectively improves reasoning performance. Furthermore, the results suggest that the frequent reasoning errors identified in this paper are common across LLMs of comparable scale.
| Comments: | 10 pages |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.15145 [cs.CL] |
| (or arXiv:2609.15145v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.15145
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Improving Mathematical Reasoning Capabilities in Large Language Models via Reasoning Process Error Classification, by Runa Yoshida and 2 other authors
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