Logical Reasoning in Large Language Models: A Survey
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.
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Hanmeng Liu
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Zhizhang Fu
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2025-02-14T11:10:54.325Z
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Zhizhang Fu
user
HarryFu
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Mengru Ding
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Ruoxi Ning
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2025-02-14T06:28:50.414Z
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ruoxining
user
ruoxining
67aeb0a3d58f4990b384d842
Chaoli Zhang
67aeb0a3d58f4990b384d843
Xiaozhang Liu
67aeb0a3d58f4990b384d844
Yue Zhang
Logical Reasoning in Large Language Models: A Survey
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.
This survey examines advancements in logical reasoning within large language models, analyzing capabilities across various paradigms and strategies for improvement.
large language models logical reasoning deductive reasoning inductive reasoning abductive reasoning analogical reasoning data-centric tuning reinforcement learning decoding strategies neuro-symbolic approaches