@inproceedings{kang-etal-2025-exploring,
    title = "Exploring Deductive and Inductive Reasoning Capabilities of Large Language Models in Procedural Planning",
    author = "Kang, Jiabao  and
      Li, Xinye  and
      Xu, Liyan  and
      Liu, Qingbin  and
      Chen, Xi  and
      Tu, Zhiying  and
      Chu, Dianhui  and
      Sui, Dianbo",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.887/",
    doi = "10.18653/v1/2025.findings-emnlp.887",
    pages = "16320--16341",
    ISBN = "979-8-89176-335-7",
    abstract = "Deductive and inductive reasoning are fundamental components of human cognition, and in daily life, people often apply these types of reasoning unconsciously. While previous studies have extensively examined the deductive and inductive reasoning abilities of Large Language Models (LLMs) in rule-based and math-related tasks, little attention has been given to their role in procedural planning{---}{---}an area that holds considerable relevance for real-world applications. To fill this gap, we present DIRPP (Deductive and Inductive Reasoning in Procedural Planning) in this paper, a benchmark designed to assess the deductive and inductive reasoning abilities of various LLMs within the context of procedural planning. Based on the benchmark, we initially observe that LLMs demonstrate excellent deductive reasoning capabilities in procedural planning but show suboptimal performance in inductive reasoning. To enhance their inductive reasoning abilities, we further propose a novel and effective method called IMSE (Induction through Multiple Similar Examples), which enables LLMs to generate multiple similar procedural plans and then perform inductive reasoning based on these examples. Through various experiments, we find that the proposed method can significantly improve the inductive reasoning capabilities of LLMs."
}