Summary of Atg: Benchmarking Automated Theorem Generation For Generative Language Models, by Xiaohan Lin et al.
ATG: Benchmarking Automated Theorem Generation for Generative Language Models
by Xiaohan Lin, Qingxing Cao, Yinya Huang, Zhicheng Yang, Zhengying Liu, Zhenguo Li, Xiaodan Liang
First submitted to arxiv on: 5 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medium Difficulty summary: This paper proposes a benchmark for Automated Theorem Generation (ATG) to evaluate whether language models can automatically generate valuable, possibly brand new, theorems that are applicable for downstream theorem proving as reusable knowledge. The ATG benchmark is constructed by splitting the Metamath library into three sets based on their proving depth. Current generative language models have achieved significant improvement in automatically proving theorems, but struggle to prove harder theorems that are distant from given hypotheses due to the exponentially growing search space. The paper conducts extensive experiments to investigate whether current LMs can generate theorems in the library and benefit problem theorems proving. The results demonstrate that high-quality ATG data facilitates models’ performances on downstream ATP. However, there is still room for current LMs to develop better ATG and generate more advanced and human-like theorems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper tries to help computers learn how to create new math problems and solve them automatically. Right now, computers are good at solving simple math problems, but they struggle with harder ones that require more complex thinking. To help computers get better, this paper creates a special test to see if computers can come up with new and useful math problems. The test uses a big library of math problems and sees how well computers can solve them. The results show that computers can do a good job when given the right kind of information, but they still need to improve to be as good as humans. |
