On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective
<|reference_start|>On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective: 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.<|reference_end|>
@article{siddiqui2024on,
    title={On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian
  Perspective},
    author={Shoaib Ahmed Siddiqui, Yanzhi Chen, Juyeon Heo, Menglin Xia, Adrian
  Weller},
    journal={arXiv preprint arXiv:2410.04541},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04541},
    primaryClass={cs.LG cs.AI}
}