  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.
