Summary of Lean-github: Compiling Github Lean Repositories For a Versatile Lean Prover, by Zijian Wu et al.
LEAN-GitHub: Compiling GitHub LEAN repositories for a versatile LEAN prover
by Zijian Wu, Jiayu Wang, Dahua Lin, Kai Chen
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel dataset, LEAN-GitHub, to aid formal mathematical reasoning. By fine-tuning InternLM-math-plus on this dataset, the authors achieve state-of-the-art performance on various Lean 4 benchmarks, including the miniF2F test, ProofNet, and Putnam. The results demonstrate the effectiveness of the proposed dataset in supporting formal reasoning across different math topics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Formal mathematical reasoning is an important area of research that requires a large amount of data to be effective. Unfortunately, extracting this data from raw formal language corpora can be time-consuming and labor-intensive. To address this issue, the authors propose a new dataset called LEAN-GitHub that contains large-scale formal data extracted from almost all Lean 4 repositories on GitHub. |
Keywords
» Artificial intelligence » Fine tuning
