@inproceedings{hua-etal-2025-inductionbench,
    title = "{I}nduction{B}ench: {LLM}s Fail in the Simplest Complexity Class",
    author = "Hua, Wenyue  and
      Wong, Tyler  and
      Sun, Fei  and
      Pan, Liangming  and
      Jardine, Adam  and
      Wang, William Yang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1287/",
    pages = "26526--26546",
    ISBN = "979-8-89176-251-0",
    abstract = "Large language models (LLMs) have shown remarkable improvements in reasoning and many existing benchmarks have been addressed by models such as o1 and o3 either fully or partially. However, a majority of these benchmarks emphasize deductive reasoning, including mathematical and coding tasks in which rules such as mathematical axioms or programming syntax are clearly defined, based on which LLMs can plan and apply these rules to arrive at a solution. In contrast, \textit{inductive reasoning}, where one infers the underlying rules from observed data, remains less explored. Such inductive processes lie at the heart of scientific discovery, as they enable researchers to extract general principles from empirical observations. To assess whether LLMs possess this capacity, we introduce \textbf{InductionBench}, a new benchmark designed to evaluate the inductive reasoning ability of LLMs. Our experimental findings reveal that even the most advanced modelw available struggle to master the simplest complexity classes within the subregular hierarchy of functions, highlighting a notable deficiency in current LLMs' inductive reasoning capabilities. Coda and data are available \url{https://anonymous.4open.science/r/inductive_reasoning_benchmark-BB2D}."
}