  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.
