Using Large Language Models for Tuning Particle Accelerators
Author Information
Author(s): Jan Kaiser, Anne Lauscher, Annika Eichler
Primary Institution: Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany
Hypothesis
Can large language models (LLMs) effectively tune particle accelerators using natural language prompts?
Conclusion
Large language models can tune particle accelerators based on natural language prompts, but they are not yet competitive with state-of-the-art tuning algorithms.
Supporting Evidence
- LLMs can tune an accelerator subsystem based on a natural language prompt.
- Performance of LLMs was compared to state-of-the-art optimization algorithms.
- LLMs showed potential for solving complex optimization tasks.
Takeaway
This study shows that computers can help adjust particle accelerators just by talking to them, but they still need to get better to match human experts.
Methodology
The study evaluated 14 different LLMs on their ability to tune a particle accelerator using various prompts and compared their performance to traditional optimization algorithms.
Limitations
LLMs are not yet competitive with traditional tuning algorithms and have high computational costs.
Digital Object Identifier (DOI)
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