CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence
Summary
CiteVQA is a benchmark for document vision-language models that evaluates both answer correctness and citation of supporting evidence, revealing widespread attribution hallucinations where models provide correct answers but cite wrong regions.
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Paper page - CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence
Source: https://huggingface.co/papers/2605.12882 Published on May 13
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Submitted byhttps://huggingface.co/zr-wang
Wangon May 18
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Abstract
CiteVQA introduces a benchmark for document vision-language models that evaluates both answer accuracy and correct citation of supporting evidence, revealing significant attribution hallucinations in current models.
Multimodal Large Language Models(MLLMs) have significantly advanceddocument understanding, yet currentDoc-VQAevaluations score only the final answer and leave the supporting evidence unchecked. This answer-only approach masks a critical failure mode: a model can land on the correct answer while grounding it in the wrong passage -- a critical risk in high-stakes domains like law, finance, and medicine, where every conclusion must be traceable to a specific source region. To address this, we introduce CiteVQA, a benchmark that requires models to return element-levelbounding-box citationsalongside each answer, evaluating both jointly. CiteVQA comprises 1,897 questions across 711 PDFs spanning seven domains and two languages, averaging 40.6 pages per document. To ensure fidelity and scalability, the ground-truth citations are generated by an automated pipeline-which identifies crucial evidence viamasking ablation-and are subsequently validated throughexpert review. At the core of our evaluation isStrict Attributed Accuracy(SAA), which credits a prediction only when the answer and the cited region are both correct. Auditing 20 MLLMs reveals a pervasiveAttribution Hallucination: models frequently produce the right answer while citing the wrong region. The strongest system (Gemini-3.1-Pro-Preview) achieves an SAA of only 76.0, and the strongest open-source MLLM reaches just 22.5. Ultimately, towards trustworthy document intelligence, CiteVQA exposes a reliability gap that answer-only evaluations overlook, providing the instrumentation needed to close it. Our repository is available at https://github.com/opendatalab/CiteVQA.
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