title: InductionBench: LLMs Fail in the Simplest Complexity Class
summary: 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.
paper_authors._id: 67bdf5d04dc920400e28c251
paper_authors.name: Wenyue Hua
paper_authors._id: 67bdf5d04dc920400e28c252
paper_authors.name: Tyler Wong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-26T08:28:57.332Z
paper_authors._id: 67be036f9d18abb027aa2f2b
paper_authors.avatarUrl: /avatars/9ba9530be87fd745b5d6f2fc05c63753.svg
paper_authors.fullname: Tyler Wong
paper_authors.type: user
paper_authors.user: Tyler-W0ng
paper_authors._id: 67bdf5d04dc920400e28c253
paper_authors.name: Sun Fei
paper_authors._id: 67bdf5d04dc920400e28c254
paper_authors.name: Liangming Pan
paper_authors._id: 67bdf5d04dc920400e28c255
paper_authors.name: Adam Jardine
paper_authors._id: 67bdf5d04dc920400e28c256
paper_authors.name: William Yang Wang
paper_title: InductionBench: LLMs Fail in the Simplest Complexity Class
paper_summary: 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.
paper_ai_summary: A new benchmark called InductionBench evaluates the inductive reasoning abilities of large language models, revealing limitations in their capacity to identify underlying rules from data.
paper_ai_keywords: large language models LLMs inductive reasoning InductionBench subregular hierarchy of functions