Summary of In Context Learning and Reasoning For Symbolic Regression with Large Language Models, by Samiha Sharlin et al.
In Context Learning and Reasoning for Symbolic Regression with Large Language Models
by Samiha Sharlin, Tyler R. Josephson
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores the potential of Large Language Models (LLMs) to perform symbolic regression, a machine-learning method for finding simple and accurate equations from datasets. The researchers prompt GPT-4 to suggest expressions from data, which are then optimized and evaluated using external Python tools. They use chain-of-thought prompting to instruct GPT-4 to analyze the data, prior expressions, and scientific context before generating new expressions. The results show that GPT-4 successfully rediscovered five well-known scientific equations from experimental data, outperforming established SR programs in simpler cases. This approach demonstrates how strategic prompting improves model performance and how natural language interface simplifies integrating theory with data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how a special type of computer program called Large Language Models can find simple formulas to describe patterns in data. The researchers use this program, GPT-4, to suggest formulas based on the data, then make adjustments and check how well it works. They also teach GPT-4 to think about the problem, the data, and scientific context before coming up with new ideas. This approach helps GPT-4 find correct formulas for five important scientific equations from experimental data. It shows that using this program can be a helpful way to combine theory and data. |
Keywords
» Artificial intelligence » Gpt » Machine learning » Prompt » Prompting » Regression
