Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal
  Document Understanding
Retrieval-Augmented Generation (RAG) systems have revolutionized information
retrieval and question answering, but traditional text-based chunking methods
struggle with complex document structures, multi-page tables, embedded figures,
and contextual dependencies across page boundaries. We present a novel
multimodal document chunking approach that leverages Large Multimodal Models
(LMMs) to process PDF documents in batches while maintaining semantic coherence
and structural integrity. Our method processes documents in configurable page
batches with cross-batch context preservation, enabling accurate handling of
tables spanning multiple pages, embedded visual elements, and procedural
content. We evaluate our approach on a curated dataset of PDF documents with
manually crafted queries, demonstrating improvements in chunk quality and
downstream RAG performance. Our vision-guided approach achieves better accuracy
compared to traditional vanilla RAG systems, with qualitative analysis showing
superior preservation of document structure and semantic coherence.
6858d76cc0c8e29df8ea3cdb
Vishesh Tripathi
claimed_verified
2025-06-23T08:14:30.878Z
638828121901766b88076aa1
https://cdn-avatars.huggingface.co/v1/production/uploads/638828121901766b88076aa1/rXlOO7eewmmaSN_hQIVz7.jpeg
Vishesh Tripathi
user
vishesh-t27
6858d76cc0c8e29df8ea3cdc
Tanmay Odapally
claimed_verified
2025-06-25T11:04:14.030Z
6658cc28e565a6bad94ca0d1
/avatars/808daea2d48322d5407d2bd0d7f95564.svg
Tanmay Odapally
user
todap
6858d76cc0c8e29df8ea3cdd
Indraneel Das
claimed_verified
2025-06-24T08:09:50.955Z
68594fd2dd18ddc6b325d9d8
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/jzsooLEGiVXLSI4sf5V-u.png
Indraneel Das
user
indraneel-das
6858d76cc0c8e29df8ea3cde
Uday Allu
claimed_verified
2025-06-23T08:14:33.310Z
64103f66928400b4164308f0
https://cdn-avatars.huggingface.co/v1/production/uploads/64103f66928400b4164308f0/5ZikDcmC1qBCWEP6YJeCx.jpeg
Uday Allu
user
udayallu
6858d76cc0c8e29df8ea3cdf
Biddwan Ahmed
Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal
  Document Understanding
Retrieval-Augmented Generation (RAG) systems have revolutionized information
retrieval and question answering, but traditional text-based chunking methods
struggle with complex document structures, multi-page tables, embedded figures,
and contextual dependencies across page boundaries. We present a novel
multimodal document chunking approach that leverages Large Multimodal Models
(LMMs) to process PDF documents in batches while maintaining semantic coherence
and structural integrity. Our method processes documents in configurable page
batches with cross-batch context preservation, enabling accurate handling of
tables spanning multiple pages, embedded visual elements, and procedural
content. We evaluate our approach on a curated dataset of PDF documents with
manually crafted queries, demonstrating improvements in chunk quality and
downstream RAG performance. Our vision-guided approach achieves better accuracy
compared to traditional vanilla RAG systems, with qualitative analysis showing
superior preservation of document structure and semantic coherence.
A novel multimodal document chunking approach using Large Multimodal Models (LMMs) enhances RAG performance by accurately processing complex PDF documents, including multi-page tables and embedded visuals.
Retrieval-Augmented Generation (RAG) Large Multimodal Models (LMMs) document chunking semantic coherence structural integrity cross-batch context preservation vision-guided approach