通过推理过程错误分类提升大型语言模型的数学推理能力

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 Authors:Runa Yoshida, Kosuke Nishida, Kyosuke Nishida 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 View PDF HTML (experimental) 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: 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 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Runa Yoshida [view email] [v1] Mon, 14 Sep 2026 07:20:44 UTC (53 KB) Full-text links: Access Paper: 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 View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)