Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, but existing approaches indiscriminately trigger retrieval and rely on single-path evidence construction, often introducing noise and limiting performance gains. In this work, we propose Decide Then Retrieve (DTR), a training-free framework that adaptively determines when retrieval is necessary and how external information should be selected. DTR leverages generation uncertainty to guide retrieval triggering and introduces a dual-path retrieval mechanism with adaptive information selection to better handle sparse and ambiguous queries. Extensive experiments across five open-domain QA benchmarks, multiple model scales, and different retrievers demonstrate that DTR consistently improves EM and F1 over standard RAG and strong retrieval-enhanced baselines, while reducing unnecessary retrievals. The code and data used in this paper are available at https://github.com/ChenWangHKU/DTR.

翻译：检索增强生成（RAG）通过引入外部知识来增强大语言模型（LLMs），但现有方法不加区分地触发检索，并依赖单一路径的证据构建，这常常引入噪声并限制了性能提升。本文提出先决策后检索（DTR），一种无训练框架，它能自适应地决定何时需要检索以及应如何选择外部信息。DTR利用生成不确定性来指导检索触发，并引入一种具有自适应信息选择能力的双路径检索机制，以更好地处理稀疏和模糊的查询。在五个开放域问答基准、多种模型规模和不同检索器上进行的大量实验表明，与标准RAG及强大的检索增强基线方法相比，DTR在提升EM和F1分数方面表现一致，同时减少了不必要的检索。本文使用的代码和数据可在 https://github.com/ChenWangHKU/DTR 获取。