LeanReasoner: Boosting Complex Logical Reasoning with Lean
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization
SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models
EviNet: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy Environments
Are LLMs Reliable Translators of Logical Reasoning Across Lexically Diversified Contexts?
Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification
Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study
토론
이 논문과 연결된 포럼 글이 아직 없습니다.