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

• URL: http://arxiv.org/abs/2410.04541v1
• Date: Sun, 6 Oct 2024 16:30:47 GMT
• Title: On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective
• Authors: Shoaib Ahmed Siddiqui, Yanzhi Chen, Juyeon Heo, Menglin Xia, Adrian Weller,
• Abstract summary: We propose a new evaluation framework to comprehensively assess Large Language Models' 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.
• Score: 37.51471397123902
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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