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DTSTART:19700308T020000
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DTSTAMP:20250626T233532Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241121T100000
DTEND;TZID=America/New_York:20241121T170000
UID:submissions.supercomputing.org_SC24_sess534_post268@linklings.com
SUMMARY:Empowering Scientific Datasets with Large Language Models
DESCRIPTION:Brian Chen and Alvin Hoang (University of California, Riversid
 e; Pacific Northwest National Laboratory (PNNL))\n\nThe growing volume and
  complexity of scientific data pose significant challenges in data managem
 ent, organization, and analysis. Our objective is to enhance the utilizati
 on of historical scientific datasets across various disciplines. To addres
 s this, we propose integrating large language models (LLMs) with databases
  to enable natural language queries, streamlined data retrieval, and analy
 sis. Leveraging LangChain, our approach harnesses the capabilities of LLMs
  and complements them with data visualization and interpretation tools. In
 itial results using Llama 3.1 70B demonstrate an 88% success rate in searc
 hing and summarizing structured text and numerical data, showcasing the po
 tential for LLM-powered tools to accelerate scientific discovery and innov
 ation.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass\
 n\nSession Chairs: Ayesha Afzal (Friedrich-Alexander University, Erlangen-
 Nuremberg; Erlangen National High Performance Computing Center); Sally Ell
 ingson (University of Kentucky); and Alan Sussman (University of Maryland)
 \n\n
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