无需坐标或区域标签的视觉文档理解中的证据归因

摘要

可靠的视觉文档理解要求模型将每个答案归因于支持该答案的证据区域。近期基准测试和系统通过坐标接口实现这一步骤：模型输出标注文档中证据区域的边界框坐标。在此接口下，视觉语言模型即使答案正确也常无法识别正确区域，这种失败被称为归因幻觉。本研究探讨了这种失败是否部分受限于模型通过坐标所能表达的内容。我们在经过验证的双语CiteVQA子集上，将坐标接口与语言接口进行比较：后者模型仅输出文本并逐字引用证据，通过多模态检索器将每个引用的位置返回为布局解析器建议的页面区域（表格和图表通过标题或注释引用）；该比较在六种开放视觉语言模型上重复进行。与坐标接口相比，证据召回率从最高8个百分点提升至26至47个百分点之间，归因幻觉率约降低一半，而答案质量几乎没有变化。基于此比较，我们使用相同的引用-检索流水线作为训练支架：由于为长文档收集区域级证据标签成本高昂，我们引入了一种GRPO策略，其奖励为裁判对标准答案和检索区域裁剪结果的解读，训练模型在无需任何区域标签的情况下生成更好的证据引用，将8B骨干模型的严格归因准确率从22.4提升至33.8。这些发现指明了一条实用路径——无需坐标接口和昂贵的区域级监督即可改进归因。

English

Reliable visual document understanding requires a model to attribute each answer to the evidence regions that support it. Recent benchmarks and systems express this step through a coordinate interface: the model outputs the coordinates of bounding boxes that mark the evidence regions in the document. Under this interface, vision-language models often fail to identify the right regions even when the answer is correct, a failure known as Attribution Hallucination. We present a study that investigates whether this failure is partially limited by what the model can express through coordinates. On a verified bilingual CiteVQA subset, we compare the coordinate interface with a language interface in which the model outputs only text, quoting its evidence verbatim, and a multimodal retriever returns the location of each quote as a page region proposed by a layout parser (tables and figures are quoted through their captions or notes); the comparison is repeated over six open vision-language models. Compared with the coordinate interface, evidence recall rises from at most 8 points to between 26 and 47 and the hallucination rate roughly halves, with little change in answer quality. Building on this comparison, we use the same quote-and-retrieve pipeline as a training scaffold: because region-level evidence labels are expensive to collect for long documents, we introduce a GRPO recipe whose reward is a judge's reading of the gold answer and crops of the retrieved regions, training the model to quote better evidence without any region labels and raising an 8B backbone's strict attributed accuracy from 22.4 to 33.8. These findings indicate a practical path to improve attribution"without a coordinate interface and without costly region-level supervision.