A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
arxiv(2024)
摘要
As one of the most advanced techniques in AI, Retrieval-Augmented Generation
(RAG) can offer reliable and up-to-date external knowledge, providing huge
convenience for numerous tasks. Particularly in the era of AI-Generated Content
(AIGC), the powerful capacity of retrieval in providing additional knowledge
enables RAG to assist existing generative AI in producing high-quality outputs.
Recently, Large Language Models (LLMs) have demonstrated revolutionary
abilities in language understanding and generation, while still facing inherent
limitations, such as hallucinations and out-of-date internal knowledge. Given
the powerful abilities of RAG in providing the latest and helpful auxiliary
information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged
to harness external and authoritative knowledge bases, rather than solely
relying on the model's internal knowledge, to augment the generation quality of
LLMs. In this survey, we comprehensively review existing research studies in
RA-LLMs, covering three primary technical perspectives: architectures, training
strategies, and applications. As the preliminary knowledge, we briefly
introduce the foundations and recent advances of LLMs. Then, to illustrate the
practical significance of RAG for LLMs, we systematically review mainstream
relevant work by their architectures, training strategies, and application
areas, detailing specifically the challenges of each and the corresponding
capabilities of RA-LLMs. Finally, to deliver deeper insights, we discuss
current limitations and several promising directions for future research.
Updated information about this survey can be found at
https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/
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