LEAN-GitHub: Compiling GitHub LEAN repositories for a versatile LEAN prover

• URL: http://arxiv.org/abs/2407.17227v1
• Date: Wed, 24 Jul 2024 12:28:03 GMT
• Title: LEAN-GitHub: Compiling GitHub LEAN repositories for a versatile LEAN prover
• Authors: Zijian Wu, Jiayu Wang, Dahua Lin, Kai Chen,
• Abstract summary: We propose LEAN-GitHub, a dataset consisting of large-scale formal data extracted from Lean 4 repositories on GitHub. Our model achieved accuracies of 48.8% with a single pass and 54.5% with 64 passes on the Lean 4 miniF2F test, surpassing state-of-the-art method at 52%.
• Score: 56.34998574538897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, large language models have presented promising results in aiding formal mathematical reasoning. However, their performance is restricted due to the scarcity of formal theorem-proving data, which requires additional effort to be extracted from raw formal language corpora. Meanwhile, a significant amount of human-written formal language corpora remains underutilized. To address this issue, we propose LEAN-GitHub, a dataset consisting of large-scale formal data extracted from almost all Lean 4 repositories on GitHub. After fine-tuning InternLM-math-plus on this dataset, our model achieved accuracies of 48.8% with a single pass and 54.5% with 64 passes on the Lean 4 miniF2F test, surpassing state-of-the-art method at 52%. And it also achieves state-of-the-art on two other Lean 4 benchmarks (ProofNet and Putnam) targeting different fields/levels of math. These results demonstrate that our proposed dataset is beneficial for formal reasoning on a wide range of math topics. We open-source our model at https://GitHub. com/InternLM/InternLM-Math and our data at https://huggingface.co/ datasets/InternLM/Lean-GitHub

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