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LeanReasoner: Boosting Complex Logical Reasoning with Lean

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arxiv 2403.13312 v1 pith:INTKWVPP submitted 2024-03-20 cs.CL

classification cs.CL
keywords leanlogicalreasoningcomplexachievesdatasetinconsistenciesmethod
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) often struggle with complex logical reasoning due to logical inconsistencies and the inherent difficulty of such reasoning. We use Lean, a theorem proving framework, to address these challenges. By formalizing logical reasoning problems into theorems within Lean, we can solve them by proving or disproving the corresponding theorems. This method reduces the risk of logical inconsistencies with the help of Lean's symbolic solver. It also enhances our ability to treat complex reasoning tasks by using Lean's extensive library of theorem proofs. Our method achieves state-of-the-art performance on the FOLIO dataset and achieves performance near this level on ProofWriter. Notably, these results were accomplished by fine-tuning on fewer than 100 in-domain samples for each dataset.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Probabilistic Circuits for Knowledge Graph Completion with Reduced Rule Sets

    cs.AI 2025-08 conditional novelty 6.0 of 10

    A probabilistic-circuit model over rule subsets lets knowledge graph completion use 70-96% fewer rules while retaining about 91% of full-rule-set accuracy.

  2. LeanTree: Accelerating White-Box Proof Search with Factorized States in Lean 4

    cs.LG 2025-07 conditional novelty 6.0 of 10

    White-box proof search with factorized Lean 4 goals reaches 18.4% on MiniF2F with Llemma-7B, outperforming black-box generation at 9.6%.

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