Evaluating LLMs for Visualization Generation and Understanding

With the amount and complexity of information produced increasing at staggering rates, information visualization is being utilized to enable people to understand and analyze information. Over the years, many techniques have been developed for creating information visualizations of different types of data. Information visualization can be created using various tools (for example, Tableau [1]), libraries in many programming languages (for example, matplotlib [2]), as well as scripts (for example, Vega-lite [3]). However, the complexity of these tools, libraries, and scripts can pose a barrier, especially for people without a strong background in data science or programming. To address this, automation of visualization creation using artificial intelligence techniques has also been explored [4]. Natural language interfaces allow users to generate visualizations using simple and intuitive commands. The integration of natural language processing into data visualization tools significantly improves the efficiency of data analysis. Analysts can now focus more on interpreting the data rather than the technicalities of creating visualizations.