SOURCE-LINKED INTELLIGENCE
LegendBench: A Diagnostic Benchmark for Legend Understanding with Counterfactual Interventions
Legends are fundamental to chart understanding, as reliable interpretation requires correctly binding legend entries to corresponding visual marks. While vision-language models (VLMs) are increasingly applied to chart understanding, their legend understanding is poorly diagnosed by aggregate accuracy, which can be satisfied by superficial shortcuts and confound legend-specific errors with other reasoning failures. To enable fine-grained diagnosis and controlled testing, we introduce LegendBench, a parametric benchmark and generation pipeline that produces targeted legend-centric test cases. Le
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T06:42:46.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.