mlsanityData to 14 Oct 2026

MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG

arXiv 2503.13563 ↗First posted 17 Mar 2025cs.CL cs.AI cs.IRcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 38 references that are in the map. First posted on arXiv 17 Mar 2025.

Builds on

28 of 38 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey29 Feb
  2. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries27 Jan

Other references · 26

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  7. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  8. 2023Vision-Language Models for Vision Tasks: A Surveycited by 839
  9. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  10. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Basescited by 429
  11. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  12. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 318
14 more
  1. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 298
  2. 2022Re-Imagen: Retrieval-Augmented Text-to-Image Generatorcited by 210
  3. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 206
  4. 2023Large-scale Multi-Modal Pre-trained Models: A Comprehensive Surveycited by 153
  5. 2022A Survey of Deep Learning for Mathematical Reasoningcited by 124
  6. 2019Multi-Sentence Argument Linkingcited by 81
  7. 2023A Survey of Large Language Models Attributioncited by 71
  8. 2022Efficient Diffusion Models for Vision: A Surveycited by 51
  9. 2024How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Promptscited by 47
  10. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  11. 2024Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using Jailbreakingcited by 30
  12. 2024MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoningcited by 12
  13. 2024Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extractioncited by 12
  14. 2024Can LLMs Deeply Detect Complex Malicious Queries? A Framework for Jailbreaking via Obfuscating Intentcited by 8

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  2. 2025Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering
  3. 2024RAG Does Not Work for Enterprises
  4. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
  5. 2024Retrieval Augmented Generation Systems: Automatic Dataset Creation, Evaluation and Boolean Agent Setup
  6. 2024T-RAG: Lessons from the LLM Trenches
  7. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  8. 2025SeCon-RAG: A Two-Stage Semantic Filtering and Conflict-Free Framework for Trustworthy RAG