Summary of In-context Symbolic Regression: Leveraging Large Language Models For Function Discovery, by Matteo Merler et al.
In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery
by Matteo Merler, Katsiaryna Haitsiukevich, Nicola Dainese, Pekka Marttinen
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 introduces a novel framework for Symbolic Regression (SR) using Large Language Models (LLMs). The proposed method, In-Context Symbolic Regression (ICSR), iteratively refines functional forms and determines coefficients using an external optimizer. Leveraging LLMs’ strong mathematical prior, ICSR proposes initial function sets given observations and refines them based on errors. Experimental results show that LLMs can successfully find symbolic equations fitting the data, matching or outperforming SR baselines on four popular benchmarks while yielding simpler equations with better out-of-distribution generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer models called Large Language Models to help us build formulas from scratch. Right now, we’re not very good at doing this without needing lots of human help. The new method they came up with uses these models to find the right formulas for a problem and then tweaks them until they fit the data perfectly. It’s like having a super smart friend who can do math really well! They tested it on four different types of problems and found that it worked just as well or even better than other methods, while also coming up with simpler answers. |
Keywords
» Artificial intelligence » Generalization » Regression
