Summary of Deepseek-prover: Advancing Theorem Proving in Llms Through Large-scale Synthetic Data, by Huajian Xin et al.
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
by Huajian Xin, Daya Guo, Zhihong Shao, Zhizhou Ren, Qihao Zhu, Bo Liu, Chong Ruan, Wenda Li, Xiaodan Liang
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper presents a novel approach to generate extensive proof data for Lean 4, a proof assistant used in mathematical verification. This is achieved by translating natural language problems into formal statements, filtering out low-quality statements, and generating proofs. The generated synthetic dataset, comprising 8 million formal statements with proofs, is then used to fine-tune the DeepSeekMath 7B model. Experimental results show that the model achieves whole-proof generation accuracies of 46.3% on the Lean 4 miniF2F test, surpassing baseline models GPT-4 and a tree search reinforcement learning method. Additionally, the model successfully proves 5 out of 148 problems in the Lean 4 Formalized International Mathematical Olympiad (FIMO) benchmark. The paper demonstrates the potential of leveraging synthetic data to enhance theorem-proving capabilities in large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at proving math problems. Right now, computers are good at doing simple math, but they struggle with more complex proofs. To help them get better, the researchers created a way to generate lots of fake math problems and their solutions. They used this data to train a special kind of computer program called a large language model. The results show that this program can solve some math problems on its own, something other programs cannot do. This is important because it could help us make computers better at solving complex math problems in the future. |
Keywords
» Artificial intelligence » Gpt » Large language model » Reinforcement learning » Synthetic data
