  Retrieval-augmented generation (RAG) methods encounter difficulties when
addressing complex questions like multi-hop queries. While iterative retrieval
methods improve performance by gathering additional information, current
approaches often rely on multiple calls of large language models (LLMs). In
this paper, we introduce EfficientRAG, an efficient retriever for multi-hop
question answering. EfficientRAG iteratively generates new queries without the
need for LLM calls at each iteration and filters out irrelevant information.
Experimental results demonstrate that EfficientRAG surpasses existing RAG
methods on three open-domain multi-hop question-answering datasets.
