可视化自动完成：通过逐步设计建议进行可视化创作
Visualization Autocomplete: Visualization Authoring via Stepwise Design Recommendations
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中文总结 AI 辅助
研究针对领域专家创建图表时确定设计下一步的难题，提出VISAUTOCOMPLETE系统，受文本自动完成启发，按顺序过程推荐步骤，用户可干预。提炼大语言模型逻辑，经评估在复杂图表创作清晰度上超基线，可接近性与大语言模型相当。
中文摘要 AI 辅助
当领域专家创建图表时，瓶颈很少是数据，而是知道图表设计的最佳下一步。可视化设计空间广阔，虽然领域专家看到好设计时能识别，但确定实现路径往往具有挑战性。为解决此问题，我们提出了VISAUTOCOMPLETE系统，它受文本自动完成启发，将可视化设计重新概念化为一个顺序过程，在创作过程的每个阶段根据常见做法推荐具体的下一步。用户可在任何步骤进行干预，或委托多个步骤给系统并从设计建议中选择一个。为支持响应式交互，我们将大语言模型的翻译逻辑提炼为一个单一函数，该函数接收当前图表状态和推荐转换作为输入，并返回更新后的图表规范作为输出。我们在图表质量和可接近性方面，将该系统与大语言模型vibecoding、Microsoft Excel和自动图表推荐引擎TaskVis进行了评估。结果表明，VisAutocomplete在复杂图表创作的清晰度方面优于所有基线，同时在可接近性方面与大语言模型相当。
英文摘要
When domain experts create charts, the bottleneck is rarely the data, but knowing the optimal next step in chart design. The visualization design space is vast, and while domain experts can recognize a good design when they see it, it is often challenging to determine the exact path to get there. To address this, we present VISAUTOCOMPLETE, a system inspired by text autocompletion that reconceptualizes visualization design as a sequential process, recommending concrete next steps at each stage of the authoring process based on common practices. Users can intervene at any step, or delegate multiple steps to the system and select one from the design recommendations. To support responsive interaction, we distill the translation logic of a large language model (LLM) into a single function that receives the current chart state and recommended transition as input and returns the updated chart specification as output. We evaluate the system against a LLM vibecoding, Microsoft Excel, and TaskVis, an automated chart recommendation engine, on chart quality and approachability. Our results show that VisAutocomplete outperforms all baselines in the articulacy of complex chart authoring, while remaining on par with LLM in approachability.
