contributing to improved model performance.
LLM Generated Content RAG
Observing that the auxiliary information recalled by RAG
is not always effective and may even have negative effects,
some studies have expanded the paradigm of RAG by delving
deeper into the internal knowledge of LLM. This approach
utilizes the content generated by LLM itself for retrieval, aim-
ing to enhance performance in downstream tasks. The follow-
ing outlines notable studies within this category:
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey