https://arxiv.org/abs/2408.00114 close this message arXiv Accessibility Forum 2024 The Accessibility Forum is back! Coming this September, the Forum is free, virtual, and open to all. Sign Up and Learn more. Sign Up Skip to main content Cornell University arXiv's Accessibility Forum starts next month! Sign Up We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2408.00114 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Artificial Intelligence arXiv:2408.00114 (cs) [Submitted on 31 Jul 2024 (v1), last revised 7 Aug 2024 (this version, v2)] Title:Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs Authors:Kewei Cheng, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Binxuan Huang, Ruirui Li, Shiyang Li, Zheng Li, Yifan Gao, Xian Li, Bing Yin, Yizhou Sun View a PDF of the paper titled Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs, by Kewei Cheng and 11 other authors View PDF HTML (experimental) Abstract:Reasoning encompasses two typical types: deductive reasoning and inductive reasoning. Despite extensive research into the reasoning capabilities of Large Language Models (LLMs), most studies have failed to rigorously differentiate between inductive and deductive reasoning, leading to a blending of the two. This raises an essential question: In LLM reasoning, which poses a greater challenge - deductive or inductive reasoning? While the deductive reasoning capabilities of LLMs, (i.e. their capacity to follow instructions in reasoning tasks), have received considerable attention, their abilities in true inductive reasoning remain largely unexplored. To investigate into the true inductive reasoning capabilities of LLMs, we propose a novel framework, SolverLearner. This framework enables LLMs to learn the underlying function (i.e., $y = f_w(x)$), that maps input data points $(x)$ to their corresponding output values $(y)$, using only in-context examples. By focusing on inductive reasoning and separating it from LLM-based deductive reasoning, we can isolate and investigate inductive reasoning of LLMs in its pure form via SolverLearner. Our observations reveal that LLMs demonstrate remarkable inductive reasoning capabilities through SolverLearner, achieving near-perfect performance with ACC of 1 in most cases. Surprisingly, despite their strong inductive reasoning abilities, LLMs tend to relatively lack deductive reasoning capabilities, particularly in tasks involving ``counterfactual'' reasoning. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2408.00114 [cs.AI] (or arXiv:2408.00114v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2408.00114 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Kewei Cheng [view email] [v1] Wed, 31 Jul 2024 18:47:11 UTC (817 KB) [v2] Wed, 7 Aug 2024 00:52:07 UTC (817 KB) Full-text links: Access Paper: View a PDF of the paper titled Inductive or Deductive? 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