Self-RAG[Asai et al., 2023b] introduces an important in-
novation called Reflection tokens. These special tokens are
generated to review the output and come in two types: Re-
trieve and Critic. The model can autonomously decide when
to retrieve paragraphs or use a set threshold to trigger re-
trieval. When retrieval is needed, the generator processes
multiple paragraphs simultaneously, performing fragment-
level beam search to obtain the best sequence. The scores for
each subdivision are updated using Critic scores, and these
weights can be adjusted during the inference process to cus-
tomize the model’s behavior. The Self-RAG framework also
allows the LLM to autonomously determine whether recall
is necessary, avoiding training additional classifiers or rely-
ing on NLI models. This enhances the model’s ability to au-
tonomously judge inputs and generate accurate answers.
7 RAG Evaluation
In exploring the development and optimization of RAG, ef-
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey