through a memory pool of examples,

Simultaneously, methods for fine-tuning generators in-
clude Self-Mem[Cheng et al., 2023b], which fine-tunes the
generator
and
Self-RAG[Asai et al., 2023b], which satisfies active re-
trieval needs by generating reflection tokens. The RA-
DIT[Lin et al., 2023] method fine-tunes both the generator
and retriever by maximizing the probability of correct an-

swers given a retrieval-enhanced directive. It updates the gen-
erator and retriever to minimize the semantic similarity be-
tween documents and queries, effectively leveraging relevant
background knowledge.
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey