Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding
This addresses the challenge of handling complex documents like multi-page tables and figures in RAG systems for information retrieval and question answering, representing an incremental improvement.
The paper tackled the problem of text-based chunking methods struggling with complex document structures in RAG systems by introducing a multimodal document chunking approach using Large Multimodal Models, resulting in improved chunk quality and downstream RAG performance with better accuracy compared to traditional systems.
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.