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This survey paper reviews retrieval-augmented generation (RAG) techniques for enhancing large language models (LLMs) in natural language processing. It addresses key challenges faced by LLMs, such as hallucination and knowledge update issues, and discusses how RAG can alleviate these problems by integrating external knowledge databases. The paper also provides insights into RAG components, training strategies, applications, and future directions for research in this area.

Author

1363477852

Language

EN