Retrieval-Augmented Generation for AI-Generated Content: A Survey

Penghao Zhao, Hailin Zhang, Qinhan Yu et al.

2024 · arXiv (Cornell University) · 82 citations

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 obje…

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