@inproceedings{liu-etal-2025-logic,
    title = "Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models",
    author = "Liu, Tongxuan  and
      Xu, Wenjiang  and
      Huang, Weizhe  and
      Zeng, Yuting  and
      Wang, Jiaxing  and
      Wang, Xingyu  and
      Yang, Hailong  and
      Li, Jing",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.510/",
    pages = "10168--10185",
    ISBN = "979-8-89176-189-6",
    abstract = "Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. Although some prompting methods, such as Chain-of-Thought, can improve the reasoning ability of LLMs to some extent, they suffer from an unfaithful issue where derived conclusions may not align with the generated reasoning chain. To address this issue, some studies employ the approach of propositional logic to further enhance logical reasoning abilities of LLMs. However, the potential omissions in the extraction of logical expressions in these methods can cause information loss in the logical reasoning process, thereby generating incorrect results. To this end, we propose Logic-of-Thought (LoT) prompting which employs propositional logic to generate expanded logical information descriptions and utilizes them as an additional augmentation to original contexts, thereby ensuring information completeness and enhancing logical reasoning ability. LoT is orthogonal to existing prompting methods and can be seamlessly integrated with them. Extensive experiments demonstrate that LoT boosts the performance of various prompting methods with a striking margin across five logical reasoning tasks. In particular, LoT enhances Chain-of-Thought{'}s performance on the ReClor dataset by +4.35{\%}, improves Chain-of-Thought with Self-Consistency{'}s performance on the RuleTaker dataset by +3.52{\%}, and boosts performance of Tree-of-Thoughts on the ProofWriter dataset by +8{\%}."
}