Summary of Llm-sr: Scientific Equation Discovery Via Programming with Large Language Models, by Parshin Shojaee et al.
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
by Parshin Shojaee, Kazem Meidani, Shashank Gupta, Amir Barati Farimani, Chandan K Reddy
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
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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 presents a novel approach, LLM-SR, to discover scientific equations from data by leveraging Large Language Models (LLMs) and domain-specific prior knowledge. The method combines the scientific priors of LLMs with evolutionary search over equation programs, iteratively proposing new equation skeleton hypotheses and optimizing them against data. This approach is demonstrated across three diverse scientific domains, achieving better fits to in-domain and out-of-domain data compared to symbolic regression baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps scientists discover equations that describe complex natural phenomena by using large language models (LLMs) and prior knowledge. The LLM suggests new equation ideas based on what it knows about science, and then adjusts those ideas to fit the data. This method is tested in three different areas of science and does better than other methods at finding the right equations. |
Keywords
» Artificial intelligence » Regression
