Retrieval-Augmented Generation for Natural Language Processing: A Survey
<|reference_start|>Retrieval-Augmented Generation for Natural Language Processing: A Survey: 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 training, including RAG with/without datastore update. Then, we introduce the application of RAG in representative natural language processing tasks and industrial scenarios. Finally, this paper discusses the future directions and challenges of RAG for promoting its development.<|reference_end|>
@article{wu2024retrieval-augmented,
    title={Retrieval-Augmented Generation for Natural Language Processing: A Survey},
    author={Shangyu Wu, Ying Xiong, Yufei Cui, Haolun Wu, Can Chen, Ye Yuan,
  Lianming Huang, Xue Liu, Tei-Wei Kuo, Nan Guan, Chun Jason Xue},
    journal={arXiv preprint arXiv:2407.13193},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13193},
    primaryClass={cs.CL}
}