评测大模型生成多视图数据界面的能力，发现其布局可复现但数据绑定和交互差。
MV-Bench: Benchmarking Multimodal Large Language Models for Coordinated Multi-View Interface Construction
- 用Tableau文件做真实标准，构建可执行的多视图界面评估体系。
- 5个主流模型在数据绑定上准确率仅21.71%，交互完整率11.68%。
- 适合关注可视化自动化、多视图逻辑理解的研究者和开发者。
多模态大语言模型（MLLMs）正被寄望于通过从视觉设计直接生成代码来自动化可视化开发。然而，现有评估主要聚焦单图表生成，忽视了需要联合推理数据语义、视图协调与交互逻辑的多视图界面构建。为此，我们提出MV-Bench，一个面向协同多视图界面构建的评估基准。不依赖不完整或不一致的开源实现，我们以Tableau工作簿文件作为真实标准，因其明确编码了数据绑定、视觉映射和交互逻辑。我们构建了多阶段流水线，将这些规范转化为可执行的网页界面。基准包含92个基础界面和1,048个经验证的实例，通过重组图表类型、数据集与交互模式生成。每个实例包含可执行代码、渲染界面、数据集及交互标注。我们在单次运行设置下评估五个先进MLLMs，采用视觉保真度、数据绑定正确性和交互完整性指标。最强模型在视觉布局复现上达到75.45%准确率，但数据绑定仅21.71%，交互完整性仅为11.68%。结果表明，当前MLLMs虽能复现外观，但在生成数据语义与交互逻辑方面仍受限。迭代优化可提升代码可执行性，但无法显著缩小数据绑定与交互生成的差距。
原文摘要 · Abstract (English)
Multimodal large language models (MLLMs) are increasingly expected to automate visualization development by generating code directly from visual designs. However, existing evaluations mainly focus on single-chart generation and overlook coordinated multi-view interface construction, which requires joint reasoning about data semantics, view coordination, and interaction logic. Consequently, MLLM capabilities in this setting remain underexplored, and the field lacks a dedicated benchmark for systematic assessment. We introduce MV-Bench, a benchmark for evaluating MLLMs on coordinated multi-view interface construction. Instead of relying on incomplete or inconsistent open-source implementations, we use Tableau workbook files as ground truth because they explicitly encode data bindings, visual mappings, and interactions. We develop a multi-stage pipeline that converts these specifications into executable web interfaces through structured intermediate representations. The benchmark contains 92 base interfaces and 1,048 verified instances created by recombining chart types, datasets, and interaction patterns. Each instance includes executable code, a rendered interface, a dataset, and interaction annotations. We evaluate five state-of-the-art MLLMs in a single-pass setting using metrics for visual fidelity, data binding correctness, and interaction completeness. The strongest model achieves 75.45 percent accuracy in visual layout reproduction, but only 21.71 percent in data binding and 11.68 percent in interaction completeness. These results show that current MLLMs can reproduce visual appearance but remain limited in generating the data semantics and interactive logic required by coordinated multi-view interfaces. Iterative refinement improves code executability but does not substantially reduce the gap in data binding and interaction generation.
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