Summary of Mining Math Conjectures From Llms: a Pruning Approach, by Jake Chuharski et al.
Mining Math Conjectures from LLMs: A Pruning Approach
by Jake Chuharski, Elias Rojas Collins, Mark Meringolo
First submitted to arxiv on: 9 Dec 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 A novel approach is proposed for generating mathematical conjectures using Large Language Models (LLMs). The focus is on the solubilizer, a recent construct in group theory. The LLMs ChatGPT, Gemini, and Claude are leveraged to generate conjectures, which are then pruned by allowing them to generate counterexamples. The results indicate that LLLMs can produce original conjectures that are either plausible or falsifiable via counterexamples, although they exhibit limitations in code execution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are helping us solve math problems in a new way! Researchers have used these powerful tools to generate math guesses called “conjectures.” They looked at something called the solubilizer, which is a recent idea in group theory. The LLMs ChatGPT, Gemini, and Claude were used to make these conjectures. Then, they checked if these guesses made sense by finding counterexamples. What’s cool is that these LLMs can create new math ideas that are either true or false. However, they’re not perfect and sometimes struggle with writing code. |
Keywords
» Artificial intelligence » Claude » Gemini
