Evaluating LLMs for Visualization Tasks

Saadiq Rauf Khan, Vinit Chandak, Sougata Mukherjea

2025

Abstract

Information Visualization has been utilized to gain insights from complex data. In recent times, Large Language Models (LLMs) have performed very well in many tasks. In this paper, we showcase the capabilities of different popular LLMs to generate code for visualization based on simple prompts. We also analyze the power of LLMs to understand some common visualizations by answering simple questions. Our study shows that LLMs could generate code for some visualizations as well as answer questions about them. However, LLMs also have several limitations. We believe that our insights can be used to improve both LLMs and Information Visualization systems.

Paper Citation

in Harvard Style

Khan S., Chandak V. and Mukherjea S. (2025). Evaluating LLMs for Visualization Tasks. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP; ISBN 978-989-758-728-3, SciTePress, pages 791-798. DOI: 10.5220/0013079600003912

in Bibtex Style

@conference{ivapp25,
author={Saadiq Khan and Vinit Chandak and Sougata Mukherjea},
title={Evaluating LLMs for Visualization Tasks},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP},
year={2025},
pages={791-798},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013079600003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP
TI - Evaluating LLMs for Visualization Tasks
SN - 978-989-758-728-3
AU - Khan S.
AU - Chandak V.
AU - Mukherjea S.
PY - 2025
SP - 791
EP - 798
DO - 10.5220/0013079600003912
PB - SciTePress