Recent works have successfully applied Large Language Models (LLMs) to function modeling tasks. However, the reasons behind this success remain unclear. In this work, we propose a new evaluation framework to comprehensively assess LLMs' function modeling abilities. By adopting a Bayesian perspective of function modeling, we discover that LLMs are relatively weak in understanding patterns in raw data, but excel at utilizing prior knowledge about the domain to develop a strong understanding of the underlying function. Our findings offer new insights about the strengths and limitations of LLMs in the context of function modeling.
@article{arxiv.2410.04541,
title = {On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective},
author = {Shoaib Ahmed Siddiqui and Yanzhi Chen and Juyeon Heo and Menglin Xia and Adrian Weller},
journal= {arXiv preprint arXiv:2410.04541},
year = {2024}
}