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@INPROCEEDINGS{Sulc:607019,
      author       = {Sulc, Antonin and Bien, Alex and Eichler, Annika and
                      Ratner, Daniel and Rehm, Florian and Mayet, Frank and
                      Hartmann, Gregor and Hoschouer, Hayden and Tuennermann,
                      Henrik and Kaiser, Jan and St. John, Jason and Maldonado,
                      Jennefer and Hazelwood, K. J. and Kammering, Raimund and
                      Hellert, Thorsten and Wilksen, Tim and Kain, Verena and Hu,
                      Wan-Lin},
      title        = {{T}owards unlocking insights from logbooks using {AI}},
      address      = {Geneva, Switzerland},
      publisher    = {JACoW Publishing},
      reportid     = {PUBDB-2024-01724, arXiv:2406.12881.
                      FERMILAB-CONF-24-0237-AD. arXiv:2406.12881.
                      FERMILAB-CONF-24-0237-AD},
      isbn         = {978-3-95450-247-9},
      pages        = {3579 - 3582},
      year         = {2024},
      note         = {5 pages, 1 figure, 15th International Particle Accelerator
                      Conference},
      comment      = {[Ebook] 15th International Particle Accelerator Conference,
                      Nashville, Tennessee : May 19-24, 2024, Nashville,
                      Tennessee, USA : proceedings / Pilat, Fulvia ; Andrian, Ivan
                      , [Geneva] : JACoW Publishing, [2024],},
      booktitle     = {[Ebook] 15th International Particle
                       Accelerator Conference, Nashville,
                       Tennessee : May 19-24, 2024, Nashville,
                       Tennessee, USA : proceedings / Pilat,
                       Fulvia ; Andrian, Ivan , [Geneva] :
                       JACoW Publishing, [2024],},
      abstract     = {Electronic logbooks contain valuable information about
                      activities and events concerning their associated particle
                      accelerator facilities. However, the highly technical nature
                      of logbook entries can hinder their usability and
                      automation. As natural language processing (NLP) continues
                      advancing, it offers opportunities to address various
                      challenges that logbooks present. This work explores jointly
                      testing a tailored Retrieval Augmented Generation (RAG)
                      model for enhancing the usability of particle accelerator
                      logbooks at institutes like DESY, BESSY, Fermilab, BNL,
                      SLAC, LBNL, and CERN. The RAG model uses a corpus built on
                      logbook contributions and aims to unlock insights from these
                      logbooks by leveraging retrieval over facility datasets,
                      including discussion about potential multimodal sources. Our
                      goals are to increase the FAIR-ness (findability,
                      accessibility, interoperability, and reusability) of
                      logbooks by exploiting their information content to
                      streamline everyday use, enable macro-analysis for root
                      cause analysis, and facilitate problem-solving automation.},
      month         = {May},
      date          = {2024-05-19},
      organization  = {15th International Particle
                       Accelerator Conference, Nashville
                       (USA), 19 May 2024 - 24 May 2024},
      keywords     = {Accelerator Physics (Other) /
                      mc8-application-of-accelerators-technology-transfer-industrial-relations-and-outreach
                      - MC8: Application of Accelerators, Technology Transfer,
                      Industrial Relations, and Outreach (Other) / MC8.U09 -
                      MC8.U09 Other Applications (Other) / acceleration (autogen)
                      / operation (autogen) / controls (autogen) / embedded
                      (autogen) / electron (autogen) / electronics (autogen) /
                      timing (autogen)},
      cin          = {MCS 4},
      cid          = {$I:(DE-H253)MCS_4-20120731$},
      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-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      eprint       = {2406.12881},
      howpublished = {arXiv:2406.12881},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2406.12881;\%\%$},
      doi          = {10.18429/JACoW-IPAC2024-THPR37},
      url          = {https://bib-pubdb1.desy.de/record/607019},
}