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是次优的。因此，确定是否进行检索对RAG至关重要，这通常被称为主动检索。然而，现有的主动检索方法面临两个挑战：1. 它们通常依赖单一标准，难以处理各种类型的指令。2. 它们依赖于专门且高度差异化的流程，因此组合使用会使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。在四种代表性用户指令类型上的实验表明，UAR在检索时机判断和下游任务性能上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。