Yanez, FernandoNobre, CarolinaArchambault, DanielNabney, IanPeltonen, Jaakko2024-05-212024-05-212024978-3-03868-256-1https://doi.org/10.2312/mlvis.20241126https://diglib.eg.org/handle/10.2312/mlvis20241126Data visualizations aim to enhance cognition and data interpretation. However, individual differences impact visual analysis, suggesting a personalized approach may be more effective. Current efforts focus on the study of generating visualizations with Large Language Models, lacking the user personalization component. This project explores using such models, specifically GPT-4, for modifying data visualizations to tailor to individual user characteristics. We developed a study to test GPT-4's ability to generate personalized visualizations. Statistical analysis of our results shows that for some personas, GPT is effective at personalizing the visualization. However, not all personalizations led to statistically significant improvements, suggesting variability in the effectiveness of LLM-driven personalization. These findings underline the importance of further exploring how personalized visualizations can best meet diverse user needs.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visualization design and evaluation methods; Information visualization; User modelsHuman centered computing → Visualization design and evaluation methodsInformation visualizationUser models10.2312/mlvis.202411265 pages