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DTSTAMP:20250626T234540Z
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DTSTART;TZID=America/New_York:20241118T153000
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UID:submissions.supercomputing.org_SC24_sess757_ws_ai4s108@linklings.com
SUMMARY:ChatVis: Automating Scientific Visualization with a Large Language
  Model
DESCRIPTION:Tanwi Mallick, Orcun Yildiz, David Lenz, and Tom Peterka (Argo
 nne National Laboratory (ANL))\n\nWe develop an iterative assistant we cal
 l ChatVis that can synthetically generate Python scripts for data analysis
  and visualization using a large language model (LLM). The assistant allow
 s a user to specify the operations in natural language, attempting to gene
 rate a Python script for the desired operations, prompting the LLM to revi
 se the script as needed until it executes correctly. The iterations includ
 e an error detection and correction mechanism that extracts error messages
  from the execution of the script and subsequently prompts LLM to correct 
 the error. Our method demonstrates correct execution on five canonical vis
 ualization scenarios, comparing results with ground truth. We also compare
 d our results with scripts generated by several other LLMs without any ass
 istance. In every instance, ChatVis successfully generated the correct scr
 ipt, whereas the unassisted LLMs failed to do so.\n\nTag: Artificial Intel
 ligence/Machine Learning\n\nRegistration Category: Workshop Reg Pass\n\nSe
 ssion Chairs: Murali Emani (Argonne National Laboratory (ANL)); Gokcen Kes
 tor (Barcelona Supercomputing Center; University of California, Merced); a
 nd Dong Li (University of California, Merced)\n\n
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