MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG

• URL: http://arxiv.org/abs/2503.13563v1
• Date: Mon, 17 Mar 2025 08:09:42 GMT
• Title: MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG
• Authors: Pingyu Wu, Daiheng Gao, Jing Tang, Huimin Chen, Wenbo Zhou, Weiming Zhang, Nenghai Yu,
• Abstract summary: We propose MES-RAG, which enhances entity-specific query handling and provides accurate, secure, and consistent responses.<n>MES-RAG introduces proactive security measures that ensure system integrity by applying protections prior to data access.<n> Experimental results demonstrate that MES-RAG significantly improves both accuracy and recall, highlighting its effectiveness in advancing the security and utility of question-answering.
• Score: 65.0423152595537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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.

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