Evaluating LLMs for Visualization Tasks
This work addresses the problem of assessing LLM capabilities for visualization tasks, which is incremental as it applies existing methods to a new domain.
The paper evaluated popular large language models (LLMs) for generating code from prompts to create visualizations and answering questions about common visualizations, finding they could perform these tasks but had several limitations.
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.