Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Wu, Pingyu, Gao, Daiheng, Tang, Jing, Chen, Huimin, Zhou, Wenbo, Zhang, Weiming, Yu, Nenghai
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979225179
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