Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are
trained on massive quantities of text parsed from the internet and have shown a
remarkable ability to respond to complex prompts in a manner often
indistinguishable from humans. We present a LLM fine-tuned on up to 40,000 data
that can predict electromagnetic spectra over a range of frequencies given a
text prompt that only specifies the metasurface geometry. Results are compared
to conventional machine learning approaches including feed-forward neural
networks, random forest, linear regression, and K-nearest neighbor (KNN).
Remarkably, the fine-tuned LLM (FT-LLM) achieves a lower error across all
dataset sizes explored compared to all machine learning approaches including a
deep neural network. We also demonstrate the LLM's ability to solve inverse
problems by providing the geometry necessary to achieve a desired spectrum.
LLMs possess some advantages over humans that may give them benefits for
research, including the ability to process enormous amounts of data, find
hidden patterns in data, and operate in higher-dimensional spaces. We propose
that fine-tuning LLMs on large datasets specific to a field allows them to
grasp the nuances of that domain, making them valuable tools for research and
analysis.

在大规模语言模型（LLMs）的研究中，通过对上万份数据进行微调，我们成功构建了一种可以根据元表面几何仅仅通过文本提示就能预测一系列频率范围内的电磁光谱的 LLM 模型。与传统的机器学习方法相比，该模型在所有探索过的数据集大小上均实现了更低的误差，包括深度神经网络。此外，我们还证明了 LLM 通过提供所需的几何形状来解决反问题的能力。因此，我们建议在特定领域的大型数据集上对 LLMs 进行微调，使其能够把握该领域的细微差别，成为研究和分析的有价值工具。