Empowering LLMs with Logical Reasoning: A Comprehensive Survey
Cheng, Fengxiang Li, Haoxuan Liu, Fenrong van Rooij, Robert Zhang, Kun Lin, Zhouchen
Cheng, Fengxiang
Li, Haoxuan
Liu, Fenrong
van Rooij, Robert
Zhang, Kun
Lin, Zhouchen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Large language models (LLMs) have achieved remarkable successes on various tasks. However, recent studies have found that there are still significant challenges to the logical reasoning abilities of LLMs, which can be categorized into the following two aspects: (1) Logical question answering: LLMs often fail to generate the correct answer within a complex logical problem which requires sophisticated deductive, inductive or abductive reasoning given a collection of premises. (2) Logical consistency: LLMs are prone to producing responses contradicting themselves across different questions. For example, a state-of-the-art question-answering LLM Macaw, answers Yes to both questions Is a magpie a bird? and Does a bird have wings? but answers No to Does a magpie have wings?. To facilitate this research direction, we comprehensively investigate the most cutting-edge methods and propose a detailed taxonomy. Specifically, to accurately answer complex logic questions, previous methods can be categorized based on reliance on external solvers, prompts, and fine-tuning. To avoid logical contradictions, we discuss concepts and solutions of various logical consistencies, including implication, negation, transitivity, factuality consistencies, and their composites. In addition, we review commonly used benchmark datasets and evaluation metrics, and discuss promising research directions, such as extending to modal logic to account for uncertainty and developing efficient algorithms that simultaneously satisfy multiple logical consistencies.
Citation
F. Cheng, H. Li, F. Liu, R. van Rooij, K. Zhang, and Z. Lin, “Empowering LLMs with Logical Reasoning: A Comprehensive Survey,” IJCAI International Joint Conference on Artificial Intelligence, vol. 2, pp. 10400–10408, Sep. 2025, doi: 10.24963/IJCAI.2025/1155
Source
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Conference
34th Internationa Joint Conference on Artificial Intelligence, IJCAI 2025
Keywords
Subjects
Source
34th Internationa Joint Conference on Artificial Intelligence, IJCAI 2025
Publisher
International Joint Conferences on Artificial Intelligence