  Autonomous tuning of particle accelerators is an active and challenging field
of research with the goal of enabling novel accelerator technologies
cutting-edge high-impact applications, such as physics discovery, cancer
research and material sciences. A key challenge with autonomous accelerator
tuning remains that the most capable algorithms require an expert in
optimisation, machine learning or a similar field to implement the algorithm
for every new tuning task. In this work, we propose the use of large language
models (LLMs) to tune particle accelerators. We demonstrate on a
proof-of-principle example the ability of LLMs to successfully and autonomously
tune a particle accelerator subsystem based on nothing more than a natural
language prompt from the operator, and compare the performance of our LLM-based
solution to state-of-the-art optimisation algorithms, such as Bayesian
optimisation (BO) and reinforcement learning-trained optimisation (RLO). In
doing so, we also show how LLMs can perform numerical optimisation of a highly
non-linear real-world objective function. Ultimately, this work represents yet
another complex task that LLMs are capable of solving and promises to help
accelerate the deployment of autonomous tuning algorithms to the day-to-day
operations of particle accelerators.
