  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.
