MHier-RAG: Multi-Modal RAG for Visual-Rich Document Question-Answering via Hierarchical and Multi-Granularity Reasoning

TL;DR

MHier-RAG is a novel multi-modal retrieval-augmented generation model that effectively integrates multi-page, multi-modal evidence for accurate question answering in visual-rich documents, addressing limitations of previous methods.

Contribution

The paper introduces MHier-RAG, a hierarchical multi-granularity retrieval and reasoning framework for multi-modal long-document question answering, combining hierarchical indexing and semantic re-ranking.

Findings

Outperforms existing methods on public datasets

Effectively connects multi-modal evidence across pages

Enhances understanding of visual-rich, multi-page documents

Abstract

The multi-modal long-context document question-answering task aims to locate and integrate multi-modal evidences (such as texts, tables, charts, images, and layouts) distributed across multiple pages, for question understanding and answer generation. The existing methods can be categorized into Large Vision-Language Model (LVLM)-based and Retrieval-Augmented Generation (RAG)-based methods. However, the former were susceptible to hallucinations, while the latter struggled for inter-modal disconnection and cross-page fragmentation. To address these challenges, a novel multi-modal RAG model, named MHier-RAG, was proposed, leveraging both textual and visual information across long-range pages to facilitate accurate question answering for visual-rich documents. A hierarchical indexing method with the integration of flattened in-page chunks and topological cross-page chunks was designed to jointly establish in-page multi-modal associations and long-distance cross-page dependencies. By means of joint similarity evaluation and large language model (LLM)-based re-ranking, a multi-granularity semantic retrieval method, including the page-level parent page retrieval and document-level summary retrieval, was proposed to foster multi-modal evidence connection and long-distance evidence integration and reasoning. Experimental results performed on public datasets, MMLongBench-Doc and LongDocURL, demonstrated the superiority of our MHier-RAG method in understanding and answering modality-rich and multi-page documents.