提升RAG精准查实体信息能力，支持多模态输出且更安全
MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG
- 引入主动安全机制，先于数据访问实施保护
- 在特定任务上准确率提升至0.83（+0.25）
- 适合需要高精度与多模态交互的问答系统开发者
检索增强生成（RAG）通过外部知识提升大语言模型性能，但难以精确检索实体信息。本文提出MES-RAG框架，增强实体查询处理能力，确保响应准确、安全且一致。该系统引入主动安全措施，在数据访问前即实施保护，保障系统完整性。同时支持文本、图像、音频、视频等实时多模态输出，可无缝集成至现有RAG架构中。实验表明，MES-RAG显著提升准确率与召回率，在特定任务上准确率达0.83，较基线提升0.25。代码与数据已开源。
原文摘要 · Abstract (English)
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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