Evaluating LLMs for Visualization Tasks

TL;DR

This paper evaluates the ability of large language models to generate visualization code and interpret visualizations, revealing their strengths and limitations in supporting data analysis tasks.

Contribution

It provides an empirical assessment of LLMs' capabilities in visualization code generation and understanding, highlighting areas for improvement.

Findings

LLMs can generate visualization code from prompts

LLMs can answer questions about visualizations

LLMs have notable limitations in visualization understanding

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.