SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understanding
Keywords: Large Multimodal Models
TL;DR: SV-RAG enhances long-document understanding by adapting MLLMs for self-visual retrieval-augmented generation, optimizing both evidence retrieval and question answering with specialized LoRA adapters.
Abstract: Multimodal large language models (MLLMs) have recently shown great progress in text-rich image understanding, yet they still struggle with complex, multi-page visually-rich documents. Traditional methods using document parsers for retrieval-augmented generation suffer from performance and efficiency limitations, while directly presenting all pages to MLLMs leads to inefficiencies, especially with lengthy ones. In this work, we present a novel framework named **S**elf-**V**isual **R**etrieval-**A**ugmented **G**eneration (SV-RAG), which can broaden horizons of *any* MLLM to support long-document understanding. We demonstrate that **MLLMs themselves can be an effective multimodal retriever** to fetch relevant pages and then answer user questions based on these pages. SV-RAG is implemented with two specific MLLM adapters, one for evidence page retrieval and the other for question answering. Empirical results show state-of-the-art performance on public benchmarks, demonstrating the effectiveness of SV-RAG.
Supplementary Material: pdf
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 12071
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