effective manner through the inclusion of RAG modules dur-
ing the inference stage. Regarding the use of augmented
data, early RAG primarily focused on the application of un-
structured data, particularly in the context of open-domain
question answering. Subsequently, the range of knowledge
sources for retrieval expanded, with the use of high-quality
data as knowledge sources effectively addressing issues such
as internalization of incorrect knowledge and hallucinations
in large models. This includes structured knowledge, with
knowledge graphs being a representative example. Recently,
there has been increased attention on self-retrieval, which in-
volves mining the knowledge of LLMs themselves to enhance
their performance.
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey