Can seeing the document like a human dramatically boost a RAG system's IQ?

Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding

Retrieval-Augmented Generation (RAG) systems have transformed information retrieval and question answering by enhancing large language models with external knowledge. However, these systems face significant limitations when processing complex documents. Traditional text-based chunking methods struggle with complex document structures, multi-page tables, embedded figures, and contextual dependencies that span across page boundaries.

A novel multimodal document chunking approach leverages Large Multimodal Models (LMMs) to process PDF documents in batches while maintaining semantic coherence and structural integrity. This 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.

The key contributions include a multimodal batch processing framework, context preservation mechanisms, techniques for maintaining structural integrity, and comprehensive evaluation on diverse document types.

The Evolution of Document Processing Techniques

Traditional RAG systems employ various chunking strategies, each with limitations. Fixed-size chunking segments documents into fixed-length pieces, often breaking coherent concepts across multiple chunks. Sentence-based chunking uses natural breakpoints but ignores document structure. Paragraph-based chunking preserves paragraph structure but struggles with complex layouts and multi-page content. Semantic chunking attempts to identify semantic boundaries but relies solely on text features, missing visual and structural elements crucial for document understanding.

Recent advances in multimodal document understanding have made significant progress through document layout analysis using vision transformers, pre-trained models like LayoutLM and LayoutLMv2, and large-scale vision foundation models. These technologies have improved the ability to process structured data within documents, though challenges remain for tables spanning multiple pages.

Previous RAG system optimization has focused on better retrieval mechanisms, query expansion techniques, re-ranking strategies, and multi-hop reasoning approaches. However, limited attention has been paid to improving the fundamental chunking process using multimodal understanding, representing a significant gap in current literature.

Vision-Guided Chunking: A Mathematical Framework

The formal problem formulation treats a PDF document D as a collection of n pages:

D={p₁,p₂,…,pₙ}

While traditional text-only chunking produces chunks C={c₁,c₂,…,cₘ} containing only textual content, the multimodal approach processes D in batches B={B₁,B₂,…,Bₖ} where each batch Bᵢ contains up to b consecutive pages (typically something like b=4).

For each batch Bᵢ, contextually-aware chunks Cᵢ are generated using a Large Multimodal Model M:

Cᵢ=M(Bᵢ,contextᵢ₋₁,prompt)

…where contextᵢ₋₁ represents relevant context from previous batches.