SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understanding

• URL: http://arxiv.org/abs/2411.01106v2
• Date: Sun, 02 Mar 2025 22:41:37 GMT
• Title: SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understanding
• Authors: Jian Chen, Ruiyi Zhang, Yufan Zhou, Tong Yu, Franck Dernoncourt, Jiuxiang Gu, Ryan A. Rossi, Changyou Chen, Tong Sun,
• Abstract summary: 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.<n>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.
• Score: 103.69014172427026
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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