Summary of Evogpt-f: An Evolutionary Gpt Framework For Benchmarking Formal Math Languages, by Johnathan Mercer
EvoGPT-f: An Evolutionary GPT Framework for Benchmarking Formal Math Languages
by Johnathan Mercer
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents EvoGPT-f, an evolutionary framework for analyzing the machine learnability of five formal math corpora (Lean 3, Lean 4, Coq, HOL 4, and HOL Light) using four tokenization methods. The study focuses on the differential machine learnability of these languages, offering a foundation for systematic comparative research across communities. By employing machine learning methodologies to aid interactive and automated theorem proving, this work advances the convergence of formal mathematics and machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computer science and math to figure out how well different math systems can be learned by machines. It looks at five main math languages (Lean 3, Lean 4, Coq, HOL 4, and HOL Light) and four ways to break them down into smaller parts that computers can understand. The goal is to see which language is the best for teaching a machine new things about math. This research will help people compare different math systems and make it easier to learn new math ideas. |
Keywords
* Artificial intelligence * Machine learning * Tokenization
