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DTSTART;TZID=America/Denver:20231113T104300
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UID:submissions.supercomputing.org_SC23_sess450_ws_worksa105@linklings.com
SUMMARY:Leveraging Large Language Models to Build and Execute Computationa
 l Workflows
DESCRIPTION:Alejandro Duque (Universidad San Francisco de Quito); Abdullah
  Syed (University of Missouri, Columbia); Kastan Day and Matthew Berry (Un
 iversity of Illinois); and Daniel S. Katz and Volodymyr Kindratenko (Unive
 rsity of Illinois, National Center for Supercomputing Applications (NCSA))
 \n\nThe recent development of large language models (LLMs) with multi-bill
 ion parameters, coupled with the creation of user-friendly application pro
 gramming interfaces (APIs), has paved the way for automatically generating
  and executing code in response to straightforward human queries. This pap
 er explores how these emerging capabilities can be harnessed to facilitate
  complex scientific workflows, eliminating the need for traditional coding
  methods. We present initial findings from our attempt to integrate Phylof
 low with OpenAI's function-calling API, and outline a strategy for develop
 ing a comprehensive workflow management system based on these concepts.\n\
 nTag: Data Analysis, Visualization, and Storage, Large Scale Systems, Prog
 ramming Frameworks and System Software, Reproducibility, Resource Manageme
 nt, Runtime Systems\n\nRegistration Category: Workshop Reg Pass\n\nSession
  Chairs: Silvina Caino-Lores (National Institute for Research in Digital S
 cience and Technology (Inria)) and Anirban Mandal (Renaissance Computing I
 nstitute (RENCI), University of North Carolina at Chapel Hill)\n\n
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