About This Document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Yunfan Gao
- sl:arxiv_num : 2312.10997
- sl:arxiv_published : 2023-12-18T07:47:33Z
- sl:arxiv_summary : Large language models (LLMs) demonstrate powerful capabilities, but they
still face challenges in practical applications, such as hallucinations, slow
knowledge updates, and lack of transparency in answers. Retrieval-Augmented
Generation (RAG) refers to the retrieval of relevant information from external
knowledge bases before answering questions with LLMs. RAG has been demonstrated
to significantly enhance answer accuracy, reduce model hallucination,
particularly for knowledge-intensive tasks. By citing sources, users can verify
the accuracy of answers and increase trust in model outputs. It also
facilitates knowledge updates and the introduction of domain-specific
knowledge. RAG effectively combines the parameterized knowledge of LLMs with
non-parameterized external knowledge bases, making it one of the most important
methods for implementing large language models. This paper outlines the
development paradigms of RAG in the era of LLMs, summarizing three paradigms:
Naive RAG, Advanced RAG, and Modular RAG. It then provides a summary and
organization of the three main components of RAG: retriever, generator, and
augmentation methods, along with key technologies in each component.
Furthermore, it discusses how to evaluate the effectiveness of RAG models,
introducing two evaluation methods for RAG, emphasizing key metrics and
abilities for evaluation, and presenting the latest automatic evaluation
framework. Finally, potential future research directions are introduced from
three aspects: vertical optimization, horizontal scalability, and the technical
stack and ecosystem of RAG.@en
- sl:arxiv_title : Retrieval-Augmented Generation for Large Language Models: A Survey@en
- sl:arxiv_updated : 2023-12-18T07:47:33Z
- sl:bookmarkOf : https://arxiv.org/abs/2312.10997
- sl:creationDate : 2023-12-23
- sl:creationTime : 2023-12-23T09:09:28Z
Documents with similar tags (experimental)