Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (AIGC). Despite its notable successes, AIGC still faces hurdles such as updating knowledge, handling long-tail data, mitigating data leakage, and managing high training and inference costs. Retrieval-Augmented Generation (RAG) has recently emerged as a paradigm to address such challenges. In particular, RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores, leading to higher accuracy and better robustness. In this paper, we comprehensively review existing efforts that integrate RAG technique into AIGC scenarios. We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators. This unified perspective encompasses all RAG scenarios, illuminating advancements and pivotal technologies that help with potential future progress. We also summarize additional enhancements methods for RAG, facilitating effective engineering and implementation of RAG systems. Then from another view, we survey on practical applications of RAG across different modalities and tasks, offering valuable references for researchers and practitioners. Furthermore, we introduce the benchmarks for RAG, discuss the limitations of current RAG systems, and suggest potential directions for future research. Github: https://github.com/PKU-DAIR/RAG-Survey.

翻译：模型算法的进步、基础模型的增长以及高质量数据集的获取，推动了人工智能生成内容（AIGC）的发展。尽管AIGC取得了显著成功，但仍面临知识更新、长尾数据处理、数据泄露缓解以及高训练与推理成本等挑战。检索增强生成（RAG）近期作为应对这些挑战的一种范式出现。特别是，RAG引入了信息检索过程，通过从可用数据存储中检索相关对象来增强生成过程，从而提升准确性和鲁棒性。本文全面回顾了将RAG技术集成到AIGC场景中的现有工作。我们首先根据检索器如何增强生成器对RAG基础进行分类，提炼出针对不同检索器和生成器的增强方法的基本抽象。这一统一视角涵盖了所有RAG场景，揭示了有助于未来潜在进展的先进技术和关键方法。我们还总结了RAG的额外增强方法，以促进RAG系统的有效工程实现与部署。随后，从另一视角，我们综述了RAG在不同模态和任务中的实际应用，为研究人员和实践者提供有价值的参考。此外，我们介绍了RAG的基准测试，讨论了当前RAG系统的局限性，并提出了未来研究的潜在方向。Github：https://github.com/PKU-DAIR/RAG-Survey。