arXiv cs.CL· Zhivar Sourati, Marianne Menglin Liu, Rongguang Wang, Zheng Wang, Yazhe Hu, Lan Guo, Yuying Wang, Mengqing Guo, Bhavesh Dnyaneshwar Gore, Shubham Pathak, Ramapriya Ranganath, Sandeep Sharma, Tao Sheng, Sujith Ravi, Dan Roth·· 2 天前AI 评分26
VELD-QA：面向长文档视觉证据定位与问答的大规模数据集
VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documents
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研究者发布 VELD-QA，一个面向长文档视觉证据定位与问答的大规模基准，包含 15,000 个人工编写问答对，覆盖 5,859 份真实 PDF、共 372,445 页，均衡涵盖图表、表格与复杂版式任务。
正文 · 原文
Authors:Zhivar Sourati, Marianne Menglin Liu, Rongguang Wang, Zheng Wang, Yazhe Hu, Lan Guo, Yuying Wang, Mengqing Guo, Bhavesh Dnyaneshwar Gore, Shubham Pathak, Ramapriya Ranganath, Sandeep Sharma, Tao Sheng, Sujith Ravi, Dan Roth
Abstract:Existing long-document QA benchmarks rarely combine large-scale human-authored questions with detailed visual evidence annotations, limiting diagnosis of evidence localization and interpretation failures. We present VELD-QA, a large-scale benchmark for Visual Evidence grounding and question answering in Long Documents. It contains 15,000 fully human-authored question-answer pairs across 5,859 real-world PDFs totaling 372,445 pages, balancing chart, table, and complex-layout tasks. Human annotations identify supporting pages and evidence elements and describe cited visual content, enabling separate retrieval and interpretation evaluation. We evaluate nine vision-language models under four evidence settings and seven retrieval-augmented and agentic systems. Our analysis identifies bottlenecks in evidence localization and interpretation. Retrieval-based methods substantially underperform the oracle setting with ground-truth supporting pages; full-document access does not close the gap. Localization alone is insufficient: oracle accuracy peaks at 81.7%, highlighting persistent visual interpretation and reasoning challenges. Enriching inputs with our visual-element annotations narrows the performance gap by 17-24%, demonstrating value beyond evaluation and motivating data enrichment for multimodal document QA. Our code is available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.20964 [cs.CV] |
| (or arXiv:2610.20964v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20964 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhivar Sourati [view email]
[v1]
Wed, 14 Oct 2026 21:36:28 UTC (590 KB)
来源：arXiv cs.CL · arxiv.org