MultiDocFusion: Hierarchical and Multimodal Chunking Pipeline for Enhanced RAG on Long Industrial Documents

📰 ArXiv cs.AI

Learn how MultiDocFusion enhances RAG on long industrial documents with hierarchical and multimodal chunking, improving answer quality and reducing information loss

advanced Published 15 Apr 2026

Action Steps

Apply vision-based document parsing to detect document regions
Extract text from detected regions using OCR or other text extraction methods
Integrate extracted text with existing text data using a multimodal chunking pipeline
Use the chunked data to fine-tune a RAG model for improved QA performance
Evaluate the performance of the RAG model on long industrial documents using metrics such as answer quality and information retention

Who Needs to Know This

NLP engineers and researchers working on RAG-based QA systems can benefit from this technique to improve their model's performance on long industrial documents, while data scientists and software engineers can apply this method to enhance their document processing pipelines

Key Insight

💡 Hierarchical and multimodal chunking can significantly improve RAG-based QA on long industrial documents by reducing information loss and improving answer quality