MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG
Pingyu Wu, Daiheng Gao, Jing Shen Tang, Huimin Chen, Wenbo Zhou, Weiming Zhang, Nenghai Yu · 2025
Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by using external knowledge, but it struggles with precise entity information retrieval.In this paper, we proposed MES-RAG framework, which 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 realtime 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.