VinQA: Visual Elements Interleaved Long-form Answer Generation for Real-World Multimodal Document QA
arXiv:2606.16092v1 Announce Type: cross Abstract: Real-world documents combine text with tables, charts, photographs, and diagrams arranged in diverse layouts, yet existing research on multimodal large language models (MLLMs) for document QA predominantly produces text-only responses, underutilizing these visual elements. We introduce VinQA, a dataset for long-form answer generation where cited visual elements are explicitly interleaved with their supporting text and grounded in relevant document pages. To support this task, we study two encoding methods for feeding raw document page images in
The proliferation of multimodal large language models and the increasing need to extract actionable insights from complex real-world documents fuel this research at the intersection of AI and data processing.
This development is crucial for strategic readers as it addresses a significant limitation in current MLLMs, enabling more comprehensive and accurate understanding of information embedded in visually rich documents.
MLLMs will move beyond text-only responses to explicitly integrate and cite visual elements, fundamentally changing how these models process and present information from multimodal sources.
- · AI document processing companies
- · Data analytics platforms
- · Enterprises with rich internal documents
- · Customers requiring detailed Q&A from complex data
- · Companies relying solely on text-based document analysis
- · Legacy OCR solutions
Improved extraction and synthesis of information from various document types, including scientific papers, financial reports, and technical manuals.
Accelerated automation of processes requiring human interpretation of complex visual-textual data, such as legal discovery or medical diagnosis support.
Enhanced AI 'understanding' of human communication leading to more sophisticated reasoning that bridges the gap between raw data and actionable intelligence.
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Read at arXiv cs.AI