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@ARTICLE{Kaiser:606814,
      author       = {Kaiser, Jan and Eichler, Annika and Lauscher, Anne},
      title        = {{L}arge {L}anguage {M}odels for {H}uman-{M}achine
                      {C}ollaborative {P}article {A}ccelerator {T}uning through
                      {N}atural {L}anguage},
      reportid     = {PUBDB-2024-01644, arXiv:2405.08888},
      year         = {2024},
      note         = {22 pages, 5 figures},
      abstract     = {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.},
      cin          = {MSK},
      cid          = {I:(DE-H253)MSK-20120731},
      pnm          = {621 - Accelerator Research and Development (POF4-621) /
                      InternLabs-0011 - HIR3X - Helmholtz International Laboratory
                      on Reliability, Repetition, Results at the most advanced
                      X-ray Sources $(2020_InternLabs-0011)$},
      pid          = {G:(DE-HGF)POF4-621 / $G:(DE-HGF)2020_InternLabs-0011$},
      experiment   = {EXP:(DE-H253)ARES-20200101},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2405.08888},
      howpublished = {arXiv:2405.08888},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2405.08888;\%\%$},
      doi          = {10.3204/PUBDB-2024-01644},
      url          = {https://bib-pubdb1.desy.de/record/606814},
}