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

2025-02-08

AI Summary

This paper addresses the lack of trustworthiness—specifically in robustness, privacy, adversarial resilience, and accountability—in Retrieval-Augmented Generation (RAG) systems. To this end, it introduces, for the first time, a unified six-dimensional analytical framework and taxonomy covering reliability, privacy, security, fairness, explainability, and accountability. The proposed multidisciplinary evaluation paradigm integrates principles from trustworthy AI, information retrieval, adversarial robustness, differential privacy, eXplainable AI (XAI), and responsibility modeling—thereby filling a critical gap in systematic RAG trustworthiness assessment. Furthermore, the work establishes an industrial-deployment-oriented RAG trustworthiness roadmap, explicitly identifying key technical bottlenecks and evolutionary pathways across all six dimensions. This provides both theoretical foundations and practical guidance for developing high-assurance AI-generated content (AIGC) systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

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.

Problem

• Addressing robustness, privacy, and accountability risks in RAG systems.
• Developing a unified framework for trustworthy RAG research.
• Enhancing reliability, fairness, and explainability in RAG applications.

Innovation

• Integrates context retrieval for content generation
• Addresses robustness, privacy, and adversarial attacks
• Provides a framework for trustworthy RAG systems