Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models

Yifan Jiang, Dae Yon Hwang, Jesse Cresswell, Freda Shi

Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models: 0 upvotes on Hugging Face Daily Papers, #52 of 60 papers on 2026-05-28. Day-by-day upvote history.

Chart question-answering (QA) benchmarks aim to pose questions that require visual reasoning to correctly answer, but models can often reach solutions through shortcuts or prior familiarity with a chart based on their own background knowledge. To strictly evaluate visual reasoning, we propose counterfactual charts where the chart-question task remains fixed, but underlying chart and the corresponding answer are varied. We introduce Chartographer, a framework to reverse engineer charts into executable code, validate reconstruction fidelity, generate seed-controlled counterfactual variants, and derive new answers from executable QA logic. We apply this framework to existing chart QA datasets and evaluate proprietary and open-source vision-language models (VLMs), measuring variation sensitivity and generalizability. Counterfactual charts reveal failures hidden by single-chart performance: VLMs often fail to generalize after answering the original chart correctly. We find failures are most prevalent when updated charts require novel visual reasoning pathways.

Paper page on Hugging Face · arXiv

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