Auto-RAG is 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.

Auto-RAG 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.

Iterative retrieval is an effective approach to building RAG systems. It's good to see more research beyond current methods that leverage few-shot prompting or manual instructions.

arxiv.org/abs/2411.19443