NL2Color: Refining Color Palettes for Charts with Natural Language

Choice of color is critical to creating effective charts with an engaging, enjoyable, and informative reading experience. However, designing a good color palette for a chart is a challenging task for novice users who lack related design expertise. For example, they often find it difficult to articulate their abstract intentions and translate these intentions into effective editing actions to achieve a desired outcome. In this work, we present NL2Color, a tool that allows novice users to refine chart color palettes using natural language expressions of their desired outcomes. We first collected and categorized a dataset of 131 triplets, each consisting of an original color palette of a chart, an editing intent, and a new color palette designed by human experts according to the intent. Our tool employs a large language model (LLM) to substitute the colors in original palettes and produce new color palettes by selecting some of the triplets as few-shot prompts. To evaluate our tool, we conducted a comprehensive two-stage evaluation, including a crowd-sourcing study ( N=71) and a within-subjects user study ( N=12). The results indicate that the quality of the color palettes revised by NL2Color has no significantly large difference from those designed by human experts. The participants who used NL2Color obtained revised color palettes to their satisfaction in a shorter period and with less effort.