InductionBench: LLMs Fail in the Simplest Complexity Class

Wenyue Hua, Fei Sun, Liangming Pan, Adam Jardine, William Yang Wang

Keywords: large language models, benchmark, inductive reasoning

Abstract: Large language models (LLMs) have shown remarkable improvements in reasoning, largely due to intensive pretraining and scaling at inference time. 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 o3, the most advanced model available, struggles to master the simplest complexity classes within the subregular hierarchy, highlighting a notable deficiency in current LLMs' inductive reasoning capabilities.

Submission Number: 145