MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG
Pingyu Wu, Daiheng Gao, Jing Tang, Huimin Chen, Wenbo Zhou, Weiming Zhang, Nenghai Yu
Abstract
Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by using external knowledge, but it struggles with precise entity information retrieval. Our proposed **MES-RAG** framework enhances entity-specific query handling and provides accurate, secure, and consistent responses. MES-RAG introduces proactive security measures that ensure system integrity by applying protections prior to data access. Additionally, the system supports real-time multi-modal outputs, including text, images, audio, and video, seamlessly integrating into existing RAG architectures. Experimental results demonstrate that MES-RAG significantly improves both accuracy and recall, highlighting its effectiveness in advancing the security and utility of question-answering, increasing accuracy to **0.83 (+0.25)** on targeted task. Our code and data are available at https://github.com/wpydcr/MES-RAG.
BibTeX
@inproceedings{wu-etal-2025-mes,
title = "{MES}-{RAG}: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to {RAG}",
author = "Wu, Pingyu and
Gao, Daiheng and
Tang, Jing and
Chen, Huimin and
Zhou, Wenbo and
Zhang, Weiming and
Yu, Nenghai",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.241/",
pages = "4287--4298",
ISBN = "979-8-89176-195-7"
}