InductionBench: LLMs Fail in the Simplest Complexity Class
Authors: name: Wenyue Hua, name: Tyler Wong, name: Sun Fei, name: Liangming Pan, name: Adam Jardine, name: William Yang Wang

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,
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 InductionBench, a new benchmark designed to evaluate the inductive
reasoning ability of LLMs. Our experimental findings reveal that even the most
advanced models 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
https://github.com/Wenyueh/inductive_reasoning_benchmark.