大型语言模型中的逻辑推理：综述
Logical Reasoning in Large Language Models: A Survey
AI总结：
本综述针对大型语言模型（LLMs）严格逻辑推理能力的开放性问题，梳理其逻辑推理的范围、理论基础与基准，分析不同推理范式的现有能力及提升策略，并指出未来探索方向以增强AI系统的逻辑推理能力。
摘要：
随着OpenAI o3和DeepSeek-R1等先进推理模型的出现，大型语言模型（LLMs）已展现出卓越的推理能力，但它们执行严格逻辑推理的能力仍是未解决的问题。本综述综合了LLMs逻辑推理领域的最新进展（AI研究的关键方向），概述了LLMs逻辑推理的范围、理论基础及评估推理能力的基准。我们分析了不同推理范式（演绎、归纳、溯因、类比）下的现有能力，并评估了提升推理性能的策略，包括数据中心调优、强化学习、解码策略和神经符号方法，最后提出未来方向，强调需进一步探索以增强AI系统的逻辑推理能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
