面向大型语言模型的可信检索增强生成：一项调研

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

February 8, 2025

作者: Bo Ni, Zheyuan Liu, Leyao Wang, Yongjia Lei, Yuying Zhao, Xueqi Cheng, Qingkai Zeng, Luna Dong, Yinglong Xia, Krishnaram Kenthapadi, Ryan Rossi, Franck Dernoncourt, Md Mehrab Tanjim, Nesreen Ahmed, Xiaorui Liu, Wenqi Fan, Erik Blasch, Yu Wang, Meng Jiang, Tyler Derr

cs.AI

摘要

检索增强生成（RAG）是一种先进技术，旨在解决人工智能生成内容（AIGC）所面临的挑战。通过将上下文检索整合到内容生成中，RAG提供可靠和最新的外部知识，减少幻觉，并确保在各种任务中具有相关上下文。然而，尽管RAG取得了成功并展现了潜力，最近的研究表明，RAG范式也带来了新的风险，包括鲁棒性问题、隐私问题、对抗性攻击和问责问题。解决这些风险对于未来RAG系统的应用至关重要，因为它们直接影响其可信度。尽管已经开发了各种方法来提高RAG方法的可信度，但在这一主题的研究中缺乏统一的视角和框架。因此，在本文中，我们旨在通过提供全面的发展值得信赖的RAG系统的路线图来填补这一空白。我们将讨论围绕五个关键视角展开：可靠性、隐私、安全性、公平性、可解释性和问责性。针对每个视角，我们提出一个通用框架和分类法，提供了一种结构化方法来理解当前挑战，评估现有解决方案，并确定有前景的未来研究方向。为了鼓励更广泛的采用和创新，我们还强调了值得信赖的RAG系统具有重大影响的下游应用。

English

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.