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Visualizationary: Automating Design Feedback for Visualization Designers Using Large Language Models.

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    Large language models (LLMs) offer customized feedback for visualization design. This study shows that even expert designers benefit from LLM-generated guidance to refine their visual communication strategies.

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    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Interactive visualization editors lack guidance on effective visual communication.
    • Large language models (LLMs) present an opportunity to provide design assistance.

    Purpose of the Study:

    • To explore the potential of LLMs for providing actionable, customized feedback to visualization designers.
    • To implement and evaluate a system (Visualizationary) that uses LLMs for visualization design support.

    Main Methods:

    • Developed Visualizationary, integrating ChatGPT with visualization design guidelines and perceptual filters.
    • Conducted a longitudinal user study with 13 visualization designers (novices, intermediates, experts) creating visualizations over several days.

    Main Results:

    • LLM-generated natural language guidance assisted designers in refining their visualizations.
    • Feedback proved valuable across different experience levels, including seasoned professionals.

    Conclusions:

    • LLMs can effectively augment the visualization design process by offering tailored, natural language feedback.
    • This approach enhances the creation of effective visual communication, even for experienced designers.