Papers by Matteo Merler
In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery (2024.acl-srw)
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| Challenge: | State of the art Symbolic Regression (SR) methods build specialized models, while the application of Large Language Models (LLMs) remains largely unexplored. |
| Approach: | They propose a framework which iteratively refines a functional form with an LLM and determines its coefficients with an external optimizer. |
| Outcome: | The proposed method outperforms the best SR methods on four popular benchmarks while yielding simpler equations with better out of distribution generalization. |