大型语言模型中的逻辑推理：一项调查

Logical Reasoning in Large Language Models: A Survey

February 13, 2025

作者: Hanmeng Liu, Zhizhang Fu, Mengru Ding, Ruoxi Ning, Chaoli Zhang, Xiaozhang Liu, Yue Zhang

cs.AI

摘要

随着像OpenAI o3和DeepSeek-R1这样的先进推理模型的出现，大型语言模型（LLMs）展示了卓越的推理能力。然而，它们在进行严格逻辑推理方面的能力仍然是一个悬而未决的问题。本调查综合了LLMs内逻辑推理的最新进展，这是人工智能研究的一个关键领域。它概述了LLMs中逻辑推理的范围、其理论基础以及用于评估推理能力的基准。我们分析了不同推理范式（演绎、归纳、诱导和类比）中现有的能力，并评估了增强推理性能的策略，包括数据中心调整、强化学习、解码策略和神经符号方法。综述最后探讨了未来的方向，强调了进一步探索以加强人工智能系统中逻辑推理的必要性。

English

With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, their ability to perform rigorous logical reasoning remains an open question. This survey synthesizes recent advancements in logical reasoning within LLMs, a critical area of AI research. It outlines the scope of logical reasoning in LLMs, its theoretical foundations, and the benchmarks used to evaluate reasoning proficiency. We analyze existing capabilities across different reasoning paradigms - deductive, inductive, abductive, and analogical - and assess strategies to enhance reasoning performance, including data-centric tuning, reinforcement learning, decoding strategies, and neuro-symbolic approaches. The review concludes with future directions, emphasizing the need for further exploration to strengthen logical reasoning in AI systems.