VizPilot：基于多模态大语言模型的SVG复合可视化自动引导系统
VizPilot: Automated Onboarding for SVG-based Composite Visualizations using Multimodal LLMs
AI总结：
VizPilot是基于多模态大语言模型的SVG复合可视化自动引导工具，通过双模块实现自动生成交互式引导，经评估可降低创作工作量并减轻用户认知负荷，提升复合可视化可用性。
AI中文摘要：
复合可视化整合多种可视化形式，可有效呈现复杂数据集，但其内在的复合设计常给新手用户带来较高的初始认知负荷。现有的可视化引导方法通常依赖特定平台、需要大量手动创作工作，且难以应对复合可视化的结构复杂性，限制了其通用性。本文提出VizPilot，一种自动可视化引导方法，可对复合可视化结构进行逆向工程，直接从原始可视化工件生成交互式引导体验。VizPilot包含两个模块：复合可视化分析器与引导界面。分析器利用多模态大语言模型（Multimodal Large Language Models, MLLMs），采用两阶段流程，将可视化分解为视觉组件、提取结构化解释，并将其映射到精确的SVG元素以实现可靠的高亮显示与交互。作为浏览器扩展实现，VizPilot仅需可视化开发者提供简短的可视化描述及可选的交互源代码，即可自动生成引导内容。引导界面支持引导式叙事滚动叙事与自由探索两种模式，使用户可逐步或按需学习可视化组件。我们通过不同输入模态的对比分析、展示创作工作量减少的使用场景，以及评估其对用户认知负荷影响的用户研究对VizPilot进行评估。结果表明，VizPilot可有效自动化引导体验的创作，同时提升复合可视化的可用性与可访问性。
英文摘要：
Composite visualizations integrate multiple visualizations to represent complex datasets effectively, but their intrinsic composite designs often impose a high initial cognitive load on novice users. Existing visualization onboarding approaches are typically platform-dependent, require substantial manual authoring effort, and struggle with the structural complexity of composite visualizations, limiting their general applicability. We present VizPilot, an automated visualization onboarding approach that reverse-engineers composite visualization structure to generate interactive onboarding experiences directly from raw visualization artifacts. VizPilot consists of two modules: a Composite Visualization Analyzer and an Onboarding Interface. Leveraging Multimodal Large Language Models (MLLMs), the Analyzer employs a two-stage pipeline that decomposes a visualization into visual components, extracts structured explanations, and maps them to precise SVG elements for reliable highlighting and interaction. Implemented as a browser extension, VizPilot requires only a brief visualization description and optional interaction source code from the visualization developer to automatically generate onboarding content. The Onboarding Interface supports both guided narrative scrollytelling and free exploration, enabling users to learn visualization components progressively or on demand. We evaluate VizPilot through a comparative analysis of different input modalities, a usage scenario demonstrating reduced authoring effort, and a user study assessing its impact on users' cognitive load. The results demonstrate that VizPilot effectively automates the authoring of onboarding experiences while improving the usability and accessibility of composite visualizations.
