mlsanityData to 14 Oct 2026

Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey

arXiv 2502.06872 ↗First posted 8 Feb 2025cs.CL cs.AIcited by 30

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retrieval into content generation, RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks. However, despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including robustness issues, privacy concerns, adversarial attacks, and accountability issues. Addressing these risks is critical for future applications of RAG systems, as they directly impact their trustworthiness. Although various methods have been developed to improve the trustworthiness of RAG methods, there is a lack of a unified perspective and framework for research in this topic. Thus, in this paper, we aim to address this gap by providing a comprehensive roadmap for developing trustworthy RAG systems. We place our discussion around five key perspectives: reliability, privacy, safety, fairness, explainability, and accountability. For each perspective, we present a general framework and taxonomy, offering a structured approach to understanding the current challenges, evaluating existing solutions, and identifying promising future research directions. To encourage broader adoption and innovation, we also highlight the downstream applications where trustworthy RAG systems have a significant impact.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 118 of its 185 references that are in the map. First posted on arXiv 8 Feb 2025.

Builds on

118 of 185 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024Faculty Perspectives on the Potential of RAG in Computer Science Higher Education28 Jul
  2. 2024FACTS About Building Retrieval Augmented Generation-based Chatbots10 Jul
  3. 2024RAGE Against the Machine: Retrieval-Augmented LLM Explanations11 May
  4. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models10 May
  5. 2024Towards a Robust Retrieval-Based Summarization System29 Mar
  6. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan

Other references · 112

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  5. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  6. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  7. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  8. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  9. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  10. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
  11. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  12. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
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  1. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
  2. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  3. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 701
  4. 2024JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Modelscited by 536
  5. 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 518
  6. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  7. 2018The Web as a Knowledge-base for Answering Complex Questionscited by 410
  8. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 401
  9. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  10. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  11. 2024Benchmarking Retrieval-Augmented Generation for Medicinecited by 331
  12. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 302
  13. 2020Overview of the TREC 2019 deep learning trackcited by 286
  14. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 271
  15. 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 256
  16. 2020Large image datasets: A pyrrhic win for computer vision?cited by 237
  17. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  18. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  19. 2023On the Reliability of Watermarks for Large Language Modelscited by 189
  20. 2024Large Language Models for Education: A Survey and Outlookcited by 188
  21. 2023Conformal Language Modelingcited by 170
  22. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 163
  23. 2023Poisoning Retrieval Corpora by Injecting Adversarial Passagescited by 159
  24. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  25. 2023Conformal Prediction with Large Language Models for Multi-Choice Question Answeringcited by 139
  26. 2024Towards Safer Large Language Models through Machine Unlearningcited by 139
  27. 2022Dense Text Retrieval based on Pretrained Language Models: A Surveycited by 136
  28. 2020Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hidingcited by 130
  29. 2023Large Language Models in Law: A Surveycited by 128
  30. 2024Benchmarking LLMs via Uncertainty Quantificationcited by 126
  31. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphscited by 119
  32. 2024LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domaincited by 118
  33. 2023The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4cited by 112
  34. 2023A Survey of Text Watermarking in the Era of Large Language Modelscited by 112
  35. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 103
  36. 2022Plex: Towards Reliability using Pretrained Large Model Extensionscited by 102
  37. 2024MarkLLM: An Open-Source Toolkit for LLM Watermarkingcited by 95
  38. 2024Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Trainingcited by 89

50 most cited of 112 All 112 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 20; 2026: 10.

30 papers.

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  1. 2025MemOS: A Memory OS for AI Systemcited by 138
  2. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 116
  3. 2025Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlookcited by 32
  4. 2025Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Modelscited by 26
  5. 2025Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Basescited by 11
  6. 2025Tools in the Loop: Quantifying Uncertainty of LLM Question Answering Systems That Use Toolscited by 9
  7. 2025Steering Multimodal Large Language Models Decoding for Context-Aware Safetycited by 7
  8. 2025Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systemscited by 6
  9. 2025Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agentscited by 6
  10. 2025Principled Context Engineering for RAG: Statistical Guarantees via Conformal Predictioncited by 6
  11. 2025Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformationcited by 3
  12. 2025RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generationcited by 3
18 more
  1. 2025Effective Damage Data Generation by Fusing Imagery with Human Knowledge Using Vision-Language Modelscited by 3
  2. 2025RAG Makes Guardrails Unsafe? Investigating Robustness of Guardrails under RAG-style Contextscited by 3
  3. 2026Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systemscited by 3
  4. 2025MrM: Black-Box Membership Inference Attacks against Multimodal RAG Systemscited by 2
  5. 2025Dual-Space Smoothness for Robust and Balanced LLM Unlearningcited by 2
  6. 2025HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generationcited by 2
  7. 2026When Confidence Takes the Wrong Path: Diagnosing Retrieval-State Lock-In in RAGcited by 2
  8. 2025Generating Medically-Informed Explanations for Depression Detection using LLMscited by 1
  9. 2026With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spotscited by 1
  10. 2025RAGFort: Dual-Path Defense Against Proprietary Knowledge Base Extraction in Retrieval-Augmented Generationcited by 0
  11. 2025Learning to Code with Context: A Study-Based Approachcited by 0
  12. 2026AstroRAG -- A Pagerank-Based Retrieval-Augmented Generation Pipeline for Question Answering in Astronomycited by 0
  13. 2026Evaluating RAG Reliability under Clean, Misleading, and Mixed Retrievalcited by 0
  14. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systemscited by 0
  15. 2026Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answeringcited by 0
  16. 2026The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systemscited by 0
  17. 2026FashionKG-RAG: Knowledge Graph-Enhanced Retrieval-Augmented Generation for Fashion Question Answeringcited by 0
  18. 2026Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocolscited by 0

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