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arXiv · 2410.04541

On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective

Shoaib Ahmed Siddiqui·Yanzhi Chen·Juyeon Heo·Menglin Xia·Adrian Weller
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Abstract

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.

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Shoaib Ahmed Siddiqui, Yanzhi Chen, Juyeon Heo, Menglin Xia, Adrian Weller. 2024-10-06. On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective. https://arxiv.org/abs/2410.04541

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