In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Therefore, determining whether to retrieve is crucial for RAG, which is usually referred to as Active Retrieval. However, existing active retrieval methods face two challenges: 1. They usually rely on a single criterion, which struggles with handling various types of instructions. 2. They depend on specialized and highly differentiated procedures, and thus combining them makes the RAG system more complicated and leads to higher response latency. To address these challenges, we propose Unified Active Retrieval (UAR). UAR contains four orthogonal criteria and casts them into plug-and-play classification tasks, which achieves multifaceted retrieval timing judgements with negligible extra inference cost. We further introduce the Unified Active Retrieval Criteria (UAR-Criteria), designed to process diverse active retrieval scenarios through a standardized procedure. Experiments on four representative types of user instructions show that UAR significantly outperforms existing work on the retrieval timing judgement and the performance of downstream tasks, which shows the effectiveness of UAR and its helpfulness to downstream tasks.

翻译：在检索增强生成（RAG）中，检索并非总是有益的，将其应用于每条指令往往并非最优选择。因此，决定是否进行检索对RAG至关重要，这一过程通常被称为主动检索。然而，现有的主动检索方法面临两大挑战：1. 它们通常依赖单一标准，难以有效处理多样化的指令类型；2. 它们依赖于专门化且高度差异化的流程，因此整合这些方法会使RAG系统更加复杂，并导致更高的响应延迟。为应对这些挑战，我们提出了统一主动检索（UAR）。UAR包含四个正交标准，并将其转化为即插即用的分类任务，从而以可忽略的额外推理成本实现多方面的检索时机判断。我们进一步引入了统一主动检索标准（UAR-Criteria），旨在通过标准化流程处理多样化的主动检索场景。在四种典型用户指令上的实验表明，UAR在检索时机判断和下游任务性能方面均显著优于现有工作，这证明了UAR的有效性及其对下游任务的助益。