Retrieval-Augmented Generation for Natural Language Processing: A Survey

• URL: http://arxiv.org/abs/2407.13193v3
• Date: Sat, 01 Mar 2025 20:23:07 GMT
• Title: Retrieval-Augmented Generation for Natural Language Processing: A Survey
• Authors: Shangyu Wu, Ying Xiong, Yufei Cui, Haolun Wu, Can Chen, Ye Yuan, Lianming Huang, Xue Liu, Tei-Wei Kuo, Nan Guan, Chun Jason Xue,
• Abstract summary: retrieval-augmented generation (RAG) leverages an external knowledge database to augment large language models (LLMs)<n>This paper reviews all significant techniques of RAG, especially in the retriever and the retrieval fusions.<n>RAG evaluation and benchmarking, as well as the application of RAG in representative NLP tasks and industrial scenarios.
• Score: 25.11304732038443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated great success in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still suffer from several key issues, such as hallucination problems, knowledge update issues, and lacking domain-specific expertise. The appearance of retrieval-augmented generation (RAG), which leverages an external knowledge database to augment LLMs, makes up those drawbacks of LLMs. This paper reviews all significant techniques of RAG, especially in the retriever and the retrieval fusions. Besides, tutorial codes are provided for implementing the representative techniques in RAG. This paper further discusses the RAG update, including RAG with/without knowledge update. Then, we introduce RAG evaluation and benchmarking, as well as the application of RAG in representative NLP tasks and industrial scenarios. Finally, this paper discusses RAG's future directions and challenges for promoting this field's development.

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