Summary of 3d-prover: Diversity Driven Theorem Proving with Determinantal Point Processes, by Sean Lamont et al.
3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes
by Sean Lamont, Christian Walder, Amir Dezfouli, Paul Montague, Michael Norrish
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO)
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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 tackles the challenge of automated formal reasoning by addressing the issue of an exponentially growing search space due to the large number of candidate proof tactics. The authors propose a novel approach called 3D-Prover that leverages synthetic data generated from previous proof attempts to prune the search and select semantically diverse and high-quality tactics using Determinantal Point Processes. This approach is designed to be general and augment any underlying tactic generator, as demonstrated on the miniF2F-valid and miniF2F-test benchmarks by augmenting the ReProver LLM. The results show an increase in proof rate, tactic success rate, execution time, and diversity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers make more logical arguments by reducing the huge number of options they consider when trying to prove something. They do this by creating a new system called 3D-Prover that uses information from previous attempts to pick the best options. This makes it faster and better at proving things, which is important for artificial intelligence. |
Keywords
» Artificial intelligence » Synthetic data
