InductionBench: LLMs Fail in the Simplest Complexity Class
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.
67bdf5d04dc920400e28c251
Wenyue Hua
claimed_verified
2025-08-06T19:39:12.823Z
639a25aba2b0b1c9d85a51e8
https://cdn-avatars.huggingface.co/v1/production/uploads/639a25aba2b0b1c9d85a51e8/pphz-MK62hPNbBkMHAkeR.jpeg
Wenyue Hua
user
wenyueH
67bdf5d04dc920400e28c252
Tyler Wong
claimed_verified
2025-02-26T08:28:57.332Z
67be036f9d18abb027aa2f2b
/avatars/9ba9530be87fd745b5d6f2fc05c63753.svg
Tyler Wong
user
Tyler-W0ng
67bdf5d04dc920400e28c253
Sun Fei
67bdf5d04dc920400e28c254
Liangming Pan
67bdf5d04dc920400e28c255
Adam Jardine
67bdf5d04dc920400e28c256
William Yang Wang
InductionBench: LLMs Fail in the Simplest Complexity Class
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.
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.
large language models LLMs inductive reasoning InductionBench subregular hierarchy of functions