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Main Authors: Khan, Saadiq Rauf, Chandak, Vinit, Mukherjea, Sougata
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2506.10996
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author Khan, Saadiq Rauf
Chandak, Vinit
Mukherjea, Sougata
author_facet Khan, Saadiq Rauf
Chandak, Vinit
Mukherjea, Sougata
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2506_10996
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Evaluating LLMs for Visualization Tasks
Khan, Saadiq Rauf
Chandak, Vinit
Mukherjea, Sougata
Software Engineering
Artificial Intelligence
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.
title Evaluating LLMs for Visualization Tasks
topic Software Engineering
Artificial Intelligence
url https://arxiv.org/abs/2506.10996