InductionBench: LLMs Fail in the Simplest Complexity Class
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916735596101632 |
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| author | Hua, Wenyue Wong, Tyler Fei, Sun Pan, Liangming Jardine, Adam Wang, William Yang |
| author_facet | Hua, Wenyue Wong, Tyler Fei, Sun Pan, Liangming Jardine, Adam Wang, William Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InductionBench: LLMs Fail in the Simplest Complexity Class Hua, Wenyue Wong, Tyler Fei, Sun Pan, Liangming Jardine, Adam Wang, William Yang Machine Learning Artificial Intelligence Computation and Language Formal Languages and Automata Theory 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. |
| title | InductionBench: LLMs Fail in the Simplest Complexity Class |
| topic | Machine Learning Artificial Intelligence Computation and Language Formal Languages and Automata Theory |
| url | https://arxiv.org/abs/2502.15823 |