# Visualizationary: Automating Design Feedback for Visualization Designers using LLMs

arXiv: https://arxiv.org/abs/2409.13109 · HTML: https://mlsanity.com/paper/2409.13109/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2024.

## Built on by (most cited first, 6 of 6)

- [CoDA: Agentic Systems for Collaborative Data Visualization](https://mlsanity.com/paper/2510.03194/) (2025)
- [Evaluating Compliance with Visualization Guidelines in Diagrams for Scientific Publications Using Large Vision Language Models](https://mlsanity.com/paper/2506.19825/) (2025)
- [From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model](https://mlsanity.com/paper/2601.19053/) (2026)
- [Do Large Language Models Understand Data Visualization Principles?](https://mlsanity.com/paper/2602.20084/) (2026)
- [Do Large Language Models Understand Data Visualization Rules?](https://mlsanity.com/paper/2602.20137/) (2026)
- [Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets](https://mlsanity.com/paper/2608.22045/) (2026)

## Abstract (arXiv)

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.