@inproceedings{wu-etal-2025-mes,
    title = "{MES}-{RAG}: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to {RAG}",
    author = "Wu, Pingyu  and
      Gao, Daiheng  and
      Tang, Jing  and
      Chen, Huimin  and
      Zhou, Wenbo  and
      Zhang, Weiming  and
      Yu, Nenghai",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.241/",
    pages = "4287--4298",
    ISBN = "979-8-89176-195-7",
    abstract = "Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by using external knowledge, but it struggles with precise entity information retrieval. Our proposed **MES-RAG** framework 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."
}