# Talk Me Through It: Developing Effective Systems for Chart Authoring

arXiv: https://arxiv.org/abs/2601.14707 · HTML: https://mlsanity.com/paper/2601.14707/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [Agentic Authoring of Interactive Multiview Visualizations in Genomics](https://mlsanity.com/paper/2606.00370/) (2026)

## Abstract (arXiv)

Recent chart-authoring systems increasingly focus on natural-language input, enabling users to form a mental image of the chart they wish to create and express this intent using spoken instructions (spoken imagined-chart data). Yet these systems are predominantly trained on typed instructions written while viewing the target chart (typed existing-chart data). While the cognitive processes for describing an existing chart arguably differ from those for creating a new chart, the structural differences in the corresponding prompts remain underexplored. We present empirical findings on the structural differences among spoken imagined-chart instructions, typed imagined-chart instructions, and typed existing-chart instructions for chart creation, showing that imagined-chart prompts contain richer command formats, element specifications, and complex linguistic features, especially in spoken instructions. We then compare the performance of systems trained on spoken imagined-chart data versus typed existing-chart data, finding that the first system outperforms the second one on both voice and text input, highlighting the necessity of targeted training on spoken imagined-chart data. We conclude with design guidelines for chart-authoring systems to improve performance in real-world scenarios.