@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://aclanthology.org/2025.findings-naacl.241/",
doi = "10.18653/v1/2025.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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wu-etal-2025-mes">
<titleInfo>
<title>MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG</title>
</titleInfo>
<name type="personal">
<namePart type="given">Pingyu</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daiheng</namePart>
<namePart type="family">Gao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jing</namePart>
<namePart type="family">Tang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Huimin</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wenbo</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Weiming</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nenghai</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: NAACL 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Luis</namePart>
<namePart type="family">Chiruzzo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alan</namePart>
<namePart type="family">Ritter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, New Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-195-7</identifier>
</relatedItem>
<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.</abstract>
<identifier type="citekey">wu-etal-2025-mes</identifier>
<identifier type="doi">10.18653/v1/2025.findings-naacl.241</identifier>
<location>
<url>https://aclanthology.org/2025.findings-naacl.241/</url>
</location>
<part>
<date>2025-04</date>
<extent unit="page">
<start>4287</start>
<end>4298</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG
%A Wu, Pingyu
%A Gao, Daiheng
%A Tang, Jing
%A Chen, Huimin
%A Zhou, Wenbo
%A Zhang, Weiming
%A Yu, Nenghai
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F wu-etal-2025-mes
%X 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.
%R 10.18653/v1/2025.findings-naacl.241
%U https://aclanthology.org/2025.findings-naacl.241/
%U https://doi.org/10.18653/v1/2025.findings-naacl.241
%P 4287-4298
Markdown (Informal)
[MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG](https://aclanthology.org/2025.findings-naacl.241/) (Wu et al., Findings 2025)
ACL
- Pingyu Wu, Daiheng Gao, Jing Tang, Huimin Chen, Wenbo Zhou, Weiming Zhang, and Nenghai Yu. 2025. MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 4287–4298, Albuquerque, New Mexico. Association for Computational Linguistics.