Visualizationary: Automating Design Feedback for Visualization Designers using LLMs

TL;DR

This paper introduces VISUALIZATIONARY, a system that leverages large language models to give personalized, actionable feedback to visualization designers, enhancing their ability to craft effective visualizations.

Contribution

It demonstrates how off-the-shelf LLMs can be integrated into visualization tools to provide design guidance and perceptual metrics, a novel application in visualization design support.

Findings

LLMs can effectively guide visualization refinement.

Designers of all skill levels benefit from natural language feedback.

The system improves visualization quality over multiple iterations.

Abstract

Interactive visualization editors empower users to author visualizations without writing code, but do not provide guidance on the art and craft of effective visual communication. In this paper, we explore the potential of using an off-the-shelf large language models (LLMs) to provide actionable and customized feedback to visualization designers. Our implementation, VISUALIZATIONARY, demonstrates how ChatGPT can be used for this purpose through two key components: a preamble of visualization design guidelines and a suite of perceptual filters that extract salient metrics from a visualization image. We present findings from a longitudinal user study involving 13 visualization designers-6 novices, 4 intermediates, and 3 experts-who authored a new visualization from scratch over several days. Our results indicate that providing guidance in natural language via an LLM can aid even seasoned designers in refining their visualizations. All our supplemental materials are available at https://osf.io/v7hu8.