Automated chart design has seen significant advancements with the emergence of Large-Language Models (LLMs), which offer a practical solution for generating charts. However, LLMs frequently introduce possibly critical design failures, such as data manipulation and confabulation. While expert users can potentially mitigate these issues through iterative prompt engineering, this process requires substantial design knowledge and significant effort, remaining a massive barrier for the general public. In this paper, we present ChartOptimiser, an automated method for generating chart designs with fidelity, efficiency, and effectiveness. Given the inter-dependencies between individual design parameters, ChartOptimiser employs Bayesian optimisation to effectively search the chart design space for a novel objective function grounded in four perceptual metrics. Our empirical evaluations in bar and pie charts demonstrate that ChartOptimiser eliminates iterative design loops, providing non-expert users with high-quality charts that outperform LLM-generated designs in chart clarity, task-solving ease, and visual aesthetics.

翻译：随着大型语言模型（LLM）的出现，自动化图表设计取得了显著进展，为图表生成提供了实用解决方案。然而，LLM 经常引入可能严重的设计缺陷，例如数据操纵和虚构。虽然专家用户可能通过迭代提示工程缓解这些问题，但该过程需要大量的设计知识和精力，对普通用户而言仍是巨大障碍。本文提出 ChartOptimiser，一种生成具有保真度、高效性和有效性的图表设计的自动化方法。鉴于各设计参数间的相互依赖性，ChartOptimiser 采用贝叶斯优化，基于四项感知指标构建的新型目标函数，有效搜索图表设计空间。我们在条形图和饼图中的实证评估表明，ChartOptimiser 消除了迭代设计循环，为非专业用户提供的高质量图表在图表清晰度、任务解决便捷性和视觉美观性方面均优于 LLM 生成的设计。