MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908273504944128 |
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| author | Wu, Pingyu Gao, Daiheng Tang, Jing Chen, Huimin Zhou, Wenbo Zhang, Weiming Yu, Nenghai |
| author_facet | Wu, Pingyu Gao, Daiheng Tang, Jing Chen, Huimin Zhou, Wenbo Zhang, Weiming Yu, Nenghai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG Wu, Pingyu Gao, Daiheng Tang, Jing Chen, Huimin Zhou, Wenbo Zhang, Weiming Yu, Nenghai Computation and Language Artificial Intelligence Information Retrieval 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. |
| title | MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2503.13563 |