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.

翻译：检索增强生成方法在处理多跳查询等复杂问题时面临挑战。虽然迭代检索方法通过收集额外信息提升了性能，但现有方法通常依赖多次调用大语言模型。本文提出EfficientRAG——一种面向多跳问答的高效检索器。该方法无需在每次迭代时调用大语言模型即可生成新查询，并能过滤无关信息。实验结果表明，EfficientRAG在三个开放域多跳问答数据集上超越了现有检索增强生成方法。