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Docmatix - a huge dataset for Document Visual Question Answering
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arXiv Computer Vision
Sep 18
DocAttriBench: Benchmarking Answer Grounding in Document Visual Question Answering
DocAttriBench (DAB) is a large‑scale benchmark for fine‑grained, element‑level source attribution in Document Visual Question Answering (VQA). It introduces MAPPET, a Mask‑based Perplexity‑Derived Attribution method that uses document layout and language modeling to identify the most informative layout element for each answer. The benchmark contains 237k documents and 296k question‑answer pairs with element‑level grounding, and it evaluates multimodal LLMs on answer accuracy, attribution accuracy, and overall answer quality, revealing that even strong models often fail to localize supporting elements.
Hugging Face Trending Papers
Sep 17
DocAttriBench: Benchmarking Answer Grounding in Document Visual Question Answering
DocAttriBench (DAB) is a large-scale benchmark that provides fine-grained, element-level source attribution for Document Visual Question Answering (VQA). It introduces MAPPET, a Mask-based Perplexity-Derived Attribution method that uses document layout and language modeling to identify the most informative layout element for each answer. The benchmark contains 237k documents and 296k question-answer pairs with grounding annotations, and it evaluates multimodal LLMs on answer accuracy, attribution accuracy, and overall answer quality.
arXiv Computer Vision
Aug 31
Doc-CoB: Enhancing Document Understanding with Visual Chain-of-Boxes Reasoning
Doc‑CoB introduces a Chain‑of‑Boxes framework that enhances document understanding by progressively focusing on query‑relevant layout regions while preserving global context. It selects key layout boxes and then applies visual prompting for deeper analysis, supported by two new reasoning tasks and an automatic pipeline that generates 249k training samples with intermediate visual supervision. Experiments across seven benchmarks and four popular models demonstrate significant performance gains, underscoring the method’s effectiveness and broad applicability.
arXiv Computation and Language
Sep 15
TestHallVQA: Exploring LVLMs' Document-Level Reasoning under Redundant Contexts from Scientific Exams
arXiv:2609.13158v1 Announce Type: new Abstract: Large Vision--Language Models (LVLMs) are increasingly expected to perform visual question answering (VQA) over planar media. However, existing planar...
arXiv Computation and Language
Aug 21
Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA
arXiv:2604. 13731v2 Announce Type: replace Abstract: Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents.
arXiv Computer Vision
Sep 1
Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation
arXiv:2608.30163v1 Announce Type: cross Abstract: Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, ex...