梳理大模型逻辑推理能力进展，揭示提升路径与未来方向。
Logical Reasoning in Large Language Models: A Survey
- 系统分析演绎、归纳、溯因等四种逻辑推理范式
- 总结数据调优、强化学习等五类增强策略
- 适合关注AI推理能力的科研与工程人员阅读
随着OpenAI o3和DeepSeek-R1等先进推理模型的出现，大型语言模型（LLMs）展现出卓越的推理能力。然而，其在严格逻辑推理方面的能力仍存疑问。本综述整合了近期在LLM逻辑推理领域的进展，涵盖该领域的研究范围、理论基础及评估基准。文章分析了不同推理范式——演绎、归纳、溯因和类比——下的现有能力，并评估了多种提升推理表现的策略，包括数据驱动调优、强化学习、解码策略以及神经符号方法。最后，论文展望了未来方向，强调需进一步探索以强化AI系统的逻辑推理能力。
原文摘要 · Abstract (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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。