Through interviews with 30 researchers, the paper identifies four roles that LLMs play in visualization design studies and maps them onto the nine-stage design study process.
Visualizationary: Automating Design Feedback for Visualization Designers using LLMs
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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.
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Qualitative Study for LLM-assisted Design Study Process: Strategies, Challenges, and Roles
Through interviews with 30 researchers, the paper identifies four roles that LLMs play in visualization design studies and maps them onto the nine-stage design study process.