Auto-RAG

an autonomous iterative retrieval model with superior performance across many datasets; Auto-RAG is a fine-tuned LLM that leverages the decision-making capabilities of an LLM; it interacts with the retriever through multiturn dialogues, systematically planning retrievals and refining queries to acquire valuable knowledge — it performs this process until sufficient external information is obtained; the authors also show that based on question difficulty, the method can adjust the number of iterations without any human intervention.