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

• URL: http://arxiv.org/abs/2502.11176v2
• Date: Wed, 09 Apr 2025 14:54:12 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.<n>We analyze the comparative dynamics of inductive, abductive, and deductive inference pipelines.<n>We investigate advanced paradigms such as hypothesis selection, verification, and refinement, revealing their potential to scale up logical inference.
• Score: 49.58786377307728
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Resources are available at https://github.com/HKUST-KnowComp/LogiDynamics.

Related papers

• Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1) [66.51642638034822]
  Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks. Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains. This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs.
  arXiv  Detail & Related papers  (2025-04-04T04:04:56Z)
• Logical Reasoning in Large Language Models: A Survey [17.06712393613964]
  This survey synthesizes recent advancements in logical reasoning in large language models (LLMs)<n>It outlines the scope of logical reasoning in LLMs, its theoretical foundations, and the benchmarks used to evaluate reasoning proficiency.<n>The review concludes with future directions, emphasizing the need for further exploration to strengthen logical reasoning in AI systems.
  arXiv  Detail & Related papers  (2025-02-13T09:19:14Z)
• Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning [1.3003982724617653]
  Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge.
  arXiv  Detail & Related papers  (2024-09-25T18:35:45Z)
• Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning [25.732397636695882]
  We show that large language models (LLMs) display reasoning patterns akin to those observed in humans. Our research demonstrates that the architecture and scale of the model significantly affect its preferred method of reasoning.
  arXiv  Detail & Related papers  (2024-02-20T12:58:14Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)
• Improving Large Language Models in Event Relation Logical Prediction [33.88499005859982]
  Event relation extraction is a challenging task that demands thorough semantic understanding and rigorous logical reasoning. In this paper, we conduct an in-depth investigation to systematically explore the capability of LLMs in understanding and applying event relation logic. Our study reveals that LLMs are not logically consistent reasoners, which results in their suboptimal performance on tasks that need rigorous reasoning.
  arXiv  Detail & Related papers  (2023-10-13T14:53:06Z)
• Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
  Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning. We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
  arXiv  Detail & Related papers  (2023-10-02T01:00:50Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Exploring Self-supervised Logic-enhanced Training for Large Language Models [59.227222647741094]
  In this paper, we make the first attempt to investigate the feasibility of incorporating logical knowledge through self-supervised post-training. We devise an auto-regressive objective variant of MERIt and integrate it with two LLM series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to 13 billion. The results on two challenging logical reasoning benchmarks demonstrate the effectiveness of LogicLLM.
  arXiv  Detail & Related papers  (2023-05-23T06:13:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.