The development of Artificial Intelligence Generated Content (AIGC) has been
facilitated by advancements in model algorithms, scalable foundation model
architectures, and the availability of ample high-quality datasets. While AIGC
has achieved remarkable performance, it still faces challenges, such as the
difficulty of maintaining up-to-date and long-tail knowledge, the risk of data
leakage, and the high costs associated with training and inference.
Retrieval-Augmented Generation (RAG) has recently emerged as a paradigm to
address such challenges. In particular, RAG introduces the information
retrieval process, which enhances AIGC results by retrieving relevant objects
from available data stores, leading to greater accuracy and 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. We distill 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. Project:
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人工智能生成内容（AIGC）发展已经通过模型算法的进展、可扩展的基础模型结构和大量高质量数据集的可用性得到促进。检索增强生成（Retrieval-Augmented Generation，RAG）作为一种范例，通过从可用数据存储中检索相关对象来增强 AIGC 结果，以提高准确性和鲁棒性。该论文综述了将 RAG 技术整合到 AIGC 场景中的现有工作，分类和总结了不同检索器和生成器的增强方法，概述了 RAG 在不同模态和任务中的实际应用，并提出了未来研究的潜在方向。