LogiDynamics: Unraveling the Dynamics of Logical Inference in Large Language Model Reasoning

• URL: http://arxiv.org/abs/2502.11176v1
• Date: Sun, 16 Feb 2025 15:54:53 GMT
• Title: LogiDynamics: Unraveling the Dynamics of Logical Inference in Large Language Model Reasoning
• Authors: Tianshi Zheng, Jiayang Cheng, Chunyang Li, Haochen Shi, Zihao Wang, Jiaxin Bai, Yangqiu Song, Ginny Y. Wong, Simon See,
• Abstract summary: This paper adopts an exploratory approach by introducing a controlled evaluation environment for analogical reasoning. We analyze the comparative dynamics of inductive, abductive, and deductive inference pipelines. We investigate advanced paradigms such as hypothesis selection, verification, and refinement, revealing their potential to scale up logical inference.
• Score: 49.58786377307728
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• Abstract: Modern large language models (LLMs) employ various forms of logical inference, both implicitly and explicitly, when addressing reasoning tasks. Understanding how to optimally leverage these inference paradigms is critical for advancing LLMs' reasoning capabilities. This paper adopts an exploratory approach by introducing a controlled evaluation environment for analogical reasoning -- a fundamental cognitive task -- that is systematically parameterized across three dimensions: modality (textual, visual, symbolic), difficulty (easy, medium, hard), and task format (multiple-choice or free-text generation). We analyze the comparative dynamics of inductive, abductive, and deductive inference pipelines across these dimensions, and demonstrate that our findings generalize to broader in-context learning tasks. Additionally, we investigate advanced paradigms such as hypothesis selection, verification, and refinement, revealing their potential to scale up logical inference in LLM reasoning. This exploratory study provides a foundation for future research in enhancing LLM reasoning through systematic logical inference strategies.

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