LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding
arXiv:2605.22829v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing multimodal RAG systems predominantly rely on coarse-grained page-level retrieval, which fails to capture fine-grained semantic and layout structures in visually rich documents, thereby compromising retrieval accuracy and leading to redundant context in downstream tasks. To address these issues, we propose Layout-oriented Fine-grained Retrieval-Augmented Generation (LFRAG), a n
The proliferation of complex, multimodal documents necessitates more sophisticated RAG techniques to fully leverage their information content with LLMs.
This development allows AI systems to extract and utilize information from documents with greater precision, improving the accuracy and efficiency of knowledge retrieval and generation.
AI models can now process visually rich documents with a fine-grained understanding of both text and layout, moving beyond simplistic page-level retrieval.
- · AI developers
- · Enterprises with rich document archives
- · Multimodal RAG platforms
- · AI systems reliant on coarse-grained retrieval
- · Less sophisticated document analysis tools
Improved performance and reduced 'hallucinations' in RAG systems when processing complex documents.
Accelerated automation of knowledge work involving document understanding and information synthesis.
Enhanced AI capabilities in fields like legal tech, medical research, and patent analysis, where document structure is critical.
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Read at arXiv cs.AI