Summary of Refactor: Learning to Extract Theorems From Proofs, by Jin Peng Zhou et al.
REFACTOR: Learning to Extract Theorems from Proofs
by Jin Peng Zhou, Yuhuai Wu, Qiyang Li, Roger Grosse
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 proposed REFACTOR method trains neural networks to recognize modular and reusable theorems in formal mathematical theorem proving, mimicking human mathematicians’ ability to extract key results. By applying REFACTOR to a set of unseen proofs, it extracts 19.6% of the theorems humans would use to write proofs. When applied to the Metamath library, REFACTOR extracted 16 new theorems, which are used frequently after refactoring, shortening proof lengths. The prover trained on the new-theorem refactored dataset proves more test theorems and outperforms state-of-the-art baselines by leveraging a diverse set of newly extracted theorems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn to recognize important math concepts like humans do. They created a new way called REFACTOR that trains computers to find these key ideas in complex math proofs. It’s like teaching a computer to identify important points in an argument. The researchers tested this method and found it could find 19.6% of the important concepts on its own, which is impressive! They also applied it to a big math library and discovered new theorems that humans hadn’t noticed before. |
