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@INPROCEEDINGS{Sulc:599188,
      author       = {Sulc, Antonin and Kammering, Raimund and Eichler, Annika
                      and Wilksen, Tim},
      title        = {{PAC}una: {A}utomated {F}ine-{T}uning of {L}anguage
                      {M}odels for {P}article {A}ccelerators},
      reportid     = {PUBDB-2023-07212},
      pages        = {7},
      year         = {2023},
      abstract     = {Navigating the landscape of particle accelerators has
                      become increasingly challenging with recent surges in
                      contributions. These intricate devices challenge
                      comprehension, even within individual facilities.To address
                      this, we introduce PACuna, a fine-tuned language model
                      refined through publicly available accelerator resources
                      like conferences, pre-prints, and books.We automated data
                      collection and question generation to minimize expert
                      involvement and make the code available.PACuna demonstrates
                      proficiency in addressing accelerator questions validated by
                      experts.Our approach shows adapting language models to
                      scientific domains by fine-tuning technical texts and
                      auto-generated corpora capturing the latest developments can
                      further produce pre-trained models to answer some specific
                      questions that commercially available assistants cannot and
                      can serve as intelligent assistants for individual
                      facilities.},
      month         = {Dec},
      date          = {2023-12-15},
      organization  = {NeurIPS 2023 workshop on Machine
                       Learning and the Physical Sciences, New
                       Orleans (USA), 15 Dec 2023 - 15 Dec
                       2023},
      cin          = {MCS 4},
      cid          = {$I:(DE-H253)MCS_4-20120731$},
      pnm          = {621 - Accelerator Research and Development (POF4-621)},
      pid          = {G:(DE-HGF)POF4-621},
      experiment   = {EXP:(DE-H253)XFEL(machine)-20150101},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://bib-pubdb1.desy.de/record/599188},
}