In Context Learning and Reasoning for Symbolic Regression with Large Language Models

Understand

Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not explicitly trained.

• Here, we explore the potential of LLMs to perform symbolic regression -- a machine-learning method for finding simple and accurate equations from datasets.
• We prompt GPT-4 and GPT-4o models to suggest expressions from data, which are then optimized and evaluated using external Python tools.
• These results are fed back to the LLMs, which propose improved expressions while optimizing for complexity and loss.