Further refinement of the RAG process during infer-
ence is seen in approaches that cater to tasks necessi-
ITRG [Feng et al., 2023] it-
tating multi-step reasoning.
eratively retrieves information to identify the correct rea-
soning paths, thereby improving task adaptability.
ITER-
RETGEN [Shao et al., 2023]
follows an iterative strat-
egy, merging retrieval and generation in a cyclical pro-
cess that alternates between “retrieval-enhanced generation”
and “generation-enhanced retrieval”. For non-knowledge-
intensive (NKI) tasks, PGRA [Guo et al., 2023] proposes a
two-stage framework, starting with a task-agnostic retriever
followed by a prompt-guided reranker to select and priori-
tize evidence. In contrast, IRCOT [Trivedi et al., 2022] com-
bines RAG with Chain of Thought (CoT) methodologies, al-
ternating CoT-guided retrievals with retrieval-informed CoT
processes, significantly boosting GPT-3’s performance across

various question-answering tasks.
RAG forLargeLanguageModels-ASurvey