Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study

• URL: http://arxiv.org/abs/2506.04810v1
• Date: Thu, 05 Jun 2025 09:34:12 GMT
• Title: Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study
• Authors: Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang,
• Abstract summary: We propose FineLogic, a fine-grained evaluation framework that assesses logical reasoning across three dimensions.<n>We conduct a study on the effects of supervision format during fine-tuning.<n>Our findings reveal that natural language supervision yields strong generalization even on out-of-distribution and long-context tasks.
• Score: 34.29839553042609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Logical reasoning is a core capability for many applications of large language models (LLMs), yet existing benchmarks often rely solely on final-answer accuracy, failing to capture the quality and structure of the reasoning process. We propose FineLogic, a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall benchmark accuracy, stepwise soundness, and representation-level alignment. In addition, to better understand how reasoning capabilities emerge, we conduct a comprehensive study on the effects of supervision format during fine-tuning. We construct four supervision styles (one natural language and three symbolic variants) and train LLMs under each. Our findings reveal that natural language supervision yields strong generalization even on out-of-distribution and long-context tasks, while symbolic reasoning styles promote more structurally sound and atomic inference chains. Further, our representation-level probing shows that fine-tuning primarily improves reasoning behaviors through step-by-step generation, rather than enhancing shortcut prediction or internalized correctness. Together, our framework and analysis provide a more rigorous and interpretable lens for evaluating and improving logical reasoning in LLMs.

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