ChartDesign: Towards LLM Designer of Data Visualization
Charts are the dominant medium for visualizing data, discovering patterns and trends, and communicating data driven insights, yet designing them still requires expensive human effort and expertise, such as selecting appropriate chart types, axis orientations, font sizes, and layouts. Most automatic visualization systems rely on handcrafted heuristics or simple rule matching and therefore struggle to generalize across domains. This work explores the potential of large language models (LLMs) as chart designers. We propose ChartDesign, which post-trains LLMs to imitate human experts and generate chart design attributes given tabular data. To this end, we curate a diverse training corpus of d...
| Authors | Mohammed Afaan Ansari, Aniruddh Bansal, Tianyi Zhou |
|---|---|
| Primary category | cs.HC |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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