DocAttriBench: Benchmarking Answer Grounding in Document Visual Question Answering
Answer grounding in document visual question answering remains an open challenge: most benchmarks lack grounding annotations or provide limited-quality labels, while constructing grounded datasets still requires costly manual effort. We introduce DocAttriBench (DAB), a large-scale benchmark for fine-grained, element-level source attribution in Document VQA, grounding answers to specific layout elements such as text blocks, tables, and images. To build DAB, we propose a Mask-based Perplexity-Derived Attribution method (MAPPET) that combines document layout and language modeling to identify the most informative element for each answer. MAPPET measures the increase in perplexity after maskin...
| Authors | Luca De Grandis, Silvia Cappelletti, William Raccagni et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 September 2026 |
| Last updated at source | 22 September 2026 |
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