SOURCE-LINKED INTELLIGENCE

Improving Mathematical Reasoning Capabilities in Large Language Models via Reasoning Process Error Classification

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

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 evalu

Read original source ↗ Open in workspace

recordType

paper

region

Global