LeanReasoner: Boosting Complex Logical Reasoning with Lean

Dongwei Jiang, Marcio Fonseca, Shay Cohen


Abstract
Large language models (LLMs) often struggle with complex logical reasoning due to logical inconsistencies and the inherent difficulty ofsuch reasoning. We use Lean, a theorem proving framework, to address these challenges. By formalizing logical reasoning problems intotheorems 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 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
Anthology ID:
2024.naacl-long.416
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7490–7503
Language:
URL:
https://aclanthology.org/2024.naacl-long.416
DOI:
Bibkey:
Cite (ACL):
Dongwei Jiang, Marcio Fonseca, and Shay Cohen. 2024. LeanReasoner: Boosting Complex Logical Reasoning with Lean. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7490–7503, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
LeanReasoner: Boosting Complex Logical Reasoning with Lean (Jiang et al., NAACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.naacl-long.416.pdf
Copyright:
 2024.naacl-long.416.copyright.pdf