Evaluating LLMs for Visualization Generation and Understanding
This work addresses the problem of assessing LLMs for visualization tasks, providing insights for researchers and practitioners in AI and information visualization, though it is incremental as it applies existing methods to new data.
The paper evaluated the capabilities of popular large language models (LLMs) in generating code for visualizations from prompts and understanding visualizations by answering questions, finding they could handle simple charts like bar and pie charts but struggled with complex ones like violin plots and made errors in tasks like identifying relationships and lengths.
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 questions. Our study shows that LLMs could generate code for some simpler visualizations such as bar and pie charts. Moreover, they could answer simple questions about visualizations. However, LLMs also have several limitations. For example, some of them had difficulty generating complex visualizations, such as violin plot. LLMs also made errors in answering some questions about visualizations, for example, identifying relationships between close boundaries and determining lengths of shapes. We believe that our insights can be used to improve both LLMs and Information Visualization systems.