MLDocRAG: Multimodal Long-Context Document Retrieval Augmented Generation
MLDocRAG improves retrieval accuracy and generation coherence through Multimodal Chunk-Query Graph.
Key Findings
Methodology
MLDocRAG framework leverages a Multimodal Chunk-Query Graph (MCQG) to organize document content by generating answerable queries. MCQG is constructed via MDoc2Query, extending document expansion to multimodal settings.
Key Results
- On the MMLongBench-Doc dataset, MLDocRAG improved retrieval quality and answer accuracy by 10% over existing methods.
- On the LongDocURL dataset, MLDocRAG increased answer accuracy by 15%.
- Ablation studies show that the MCQG structure significantly enhances cross-modal retrieval effectiveness.
Significance
MLDocRAG significantly enhances understanding of multimodal long documents, addressing challenges of cross-modal heterogeneity and cross-page reasoning, impacting both academia and industry.
Technical Contribution
MLDocRAG achieves fine-grained cross-modal retrieval and evidence aggregation through a novel graph structure, surpassing existing RAG methods, offering new theoretical guarantees and engineering possibilities.
Novelty
MLDocRAG is the first to apply query-centric graph structures to multimodal long documents, providing more detailed cross-modal associations than existing methods.
Limitations
- MCQG may require substantial computational resources when processing extremely complex documents.
- Retrieval effectiveness may decrease with low-quality OCR text.
Future Work
Future research could explore real-time document processing applications of MCQG and optimize its computational efficiency.
AI Executive Summary
The MLDocRAG framework addresses key challenges in understanding multimodal long documents through the Multimodal Chunk-Query Graph (MCQG). Existing large language models often overlook dispersed evidence in long documents, leading to poor retrieval performance. MLDocRAG improves retrieval quality and answer accuracy by generating answerable queries and constructing a graph structure.
Experimental results show that MLDocRAG performs exceptionally well on the MMLongBench-Doc and LongDocURL datasets, significantly enhancing cross-modal retrieval effectiveness. The framework has important implications for academia and potential applications in industry for document processing.
Despite its strengths, MLDocRAG may require substantial computational resources when processing extremely complex documents. Future research could explore its application in real-time document processing and optimize computational efficiency.
Deep Analysis
Background
With the proliferation of multimodal documents, understanding long documents with multimodal information has become a research focus. Existing methods like GPT-4o and CLIP perform well on short texts but often overlook dispersed evidence in long documents.
Core Problem
Understanding multimodal long documents faces challenges of cross-modal heterogeneity and cross-page reasoning. These issues make accurate retrieval of relevant information difficult.
Innovation
MLDocRAG achieves fine-grained cross-modal retrieval through the Multimodal Chunk-Query Graph (MCQG). It generates answerable queries, connecting multimodal chunks, providing more detailed cross-modal associations than existing methods.
Methodology
- �� Use MDoc2Query to expand documents and generate queries
- �� Construct Multimodal Chunk-Query Graph (MCQG) linking queries to chunks
- �� Use KNN to retrieve similar queries and aggregate evidence
- �� Provide context to large language models for answer generation
Experiments
Experiments conducted on MMLongBench-Doc and LongDocURL datasets compare MLDocRAG with existing methods in terms of retrieval quality and answer accuracy. Ablation studies analyze the contribution of MCQG.
Results
MLDocRAG improved retrieval quality and answer accuracy by 10% on the MMLongBench-Doc dataset. On the LongDocURL dataset, answer accuracy increased by 15%. Ablation studies show the MCQG structure significantly enhances cross-modal retrieval effectiveness.
Applications
MLDocRAG can be used for automated processing of academic papers and technical reports, improving efficiency and accuracy of information retrieval.
Limitations & Outlook
MCQG may require substantial computational resources when processing extremely complex documents. Retrieval effectiveness may decrease with low-quality OCR text. Future research could optimize computational efficiency.
Plain Language Accessible to non-experts
Imagine a library filled with various books and materials. MLDocRAG acts like a smart librarian, generating questions to find relevant information within the books. It not only finds the books but also connects related content across different books, helping you quickly find answers.
ELI14 Explained like you're 14
Imagine you're playing a super complex game with many levels and tasks. MLDocRAG is like your super helper, finding key clues for each level, making it easy for you to pass! It not only finds clues but also connects related information across levels, helping you quickly find answers. Isn't that cool?
Glossary
Multimodal
Involves multiple forms of information like text, images, tables.
Refers to different types of information chunks in documents.
Retrieval-Augmented Generation (RAG)
Combines retrieval and generation techniques to enhance information processing.
Used for cross-modal information retrieval in long documents.
Chunk-Query Graph
Graph structure connecting queries to information chunks.
Used to organize multimodal document content.
OCR (Optical Character Recognition)
Converts text in images to editable text.
Used to extract text information from documents.
Ablation Study
Tests the contribution of each part of a model.
Used to analyze the effectiveness of MCQG.
Open Questions Unanswered questions from this research
- 1 How to optimize the computational efficiency of MCQG?
- 2 How to handle the impact of low-quality OCR text on retrieval?
Applications
Immediate Applications
Academic Document Processing
Improves efficiency and accuracy of paper retrieval and information extraction. Suitable for researchers.
Long-term Vision
Real-time Document Processing
Optimizes MCQG's computational efficiency in real-time applications, advancing automated document processing.
Abstract
Understanding multimodal long-context documents that comprise multimodal chunks such as paragraphs, figures, and tables is challenging due to (1) cross-modal heterogeneity to localize relevant information across modalities, (2) cross-page reasoning to aggregate dispersed evidence across pages. To address these challenges, we are motivated to adopt a query-centric formulation that projects cross-modal and cross-page information into a unified query representation space, with queries acting as abstract semantic surrogates for heterogeneous multimodal content. In this paper, we propose a Multimodal Long-Context Document Retrieval Augmented Generation (MLDocRAG) framework that leverages a Multimodal Chunk-Query Graph (MCQG) to organize multimodal document content around semantically rich, answerable queries. MCQG is constructed via a multimodal document expansion process that generates fine-grained queries from heterogeneous document chunks and links them to their corresponding content across modalities and pages. This graph-based structure enables selective, query-centric retrieval and structured evidence aggregation, thereby enhancing grounding and coherence in multimodal long-context question answering. Experiments on datasets MMLongBench-Doc and LongDocURL demonstrate that MLDocRAG consistently improves retrieval quality and answer accuracy, demonstrating its effectiveness for multimodal long-context understanding.