Mathematical discoveries from program search with large language models
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) can now make scientific discoveries using FunSearch, an evolutionary method that pairs LLMs with evaluators to overcome confabulations and solve complex problems.
Area Of Science
- Artificial Intelligence
- Combinatorics
- Computer Science
Background
- Large language models (LLMs) excel at complex tasks but often confabulate, limiting their scientific application.
- Existing LLM approaches struggle with scientific discovery due to inaccuracies.
Purpose Of The Study
- Introduce FunSearch, an evolutionary procedure to enhance LLM capabilities for scientific discovery.
- Demonstrate FunSearch's effectiveness in solving established open problems.
Main Methods
- Pairing a pretrained LLM with a systematic evaluator in an evolutionary search.
- Applying FunSearch to extremal combinatorics (cap set problem) and algorithmic problems (online bin packing).
Main Results
- Discovered new, improved constructions for large cap sets in both finite dimensional and asymptotic cases.
- Identified novel heuristics for online bin packing, outperforming existing baselines.
- Showcased FunSearch's ability to generate interpretable programs for problem-solving.
Conclusions
- FunSearch enables LLMs to make significant discoveries in established scientific domains.
- The approach overcomes LLM confabulations, enhancing reliability for scientific applications.
- FunSearch offers a scalable and interpretable method for AI-driven scientific discovery.
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