用可视化还原原始数据来自动评估生成质量。
The Truth, the Whole Truth, and Nothing but the Truth: Automatic Visualization Evaluation from Reconstruction Quality
- 从可视化中重建原始数据，以衡量生成质量。
- 无需人工标注，可大规模自动化评估。
- 适合需要高效迭代的AI绘图工作流使用。
近年来，AI技术使通过文本提示直接生成可视化成为可能，但单次生成方法常因质量不足而需人工修正。人工评估虽有效，却成本高昂且难以扩展。为此，我们提出一种无需依赖大量人工标注数据集的自动化评估方法。该方法利用原始数据作为隐式真实值，通过评估从可视化中重建原始数据的准确性来衡量可视化质量。这一基于重建的指标为全面的人工评估提供了自主、可扩展的替代方案，有助于提升AI驱动可视化流程的效率与可靠性。
原文摘要 · Abstract (English)
Recent advances in AI enable the automatic generation of visualizations directly from textual prompts using agentic workflows. However, visualizations produced via one-shot generative methods often suffer from insufficient quality, typically requiring a human in the loop to refine the outputs. Human evaluation, though effective, is costly and impractical at scale. To alleviate this problem, we propose an automated metric that evaluates visualization quality without relying on extensive human-labeled datasets. Instead, our approach uses the original underlying data as implicit ground truth. Specifically, we introduce a method that measures visualization quality by assessing the reconstruction accuracy of the original data from the visualization itself. This reconstruction-based metric provides an autonomous and scalable proxy for thorough human evaluation, facilitating more efficient and reliable AI-driven visualization workflows.
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