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Recent studies have shown that dense retrieval models, lacking dedicatedtraining data, struggle to perform well across diverse retrieval tasks, asdifferent retrieval tasks often entail distinct search intents. To address thischallenge, in this work we introduce ControlRetriever, a generic and efficientapproach with a parameter isolated architecture, capable of controlling denseretrieval models to directly perform varied retrieval tasks, harnessing thepower of instructions that explicitly describe retrieval intents in naturallanguage. Leveraging the foundation of ControlNet, which has proven powerful intext-to-image generation, ControlRetriever imbues different retrieval modelswith the new capacity of controllable retrieval, all while being guided bytask-specific instructions. Furthermore, we propose a novel LLM guidedInstruction Synthesizing and Iterative Training strategy, which iterativelytunes ControlRetriever based on extensive automatically-generated retrievaldata with diverse instructions by capitalizing the advancement of largelanguage models. Extensive experiments show that in the BEIR benchmark, withonly natural language descriptions of specific retrieval intent for each task,ControlRetriever, as a unified multi-task retrieval system withouttask-specific tuning, significantly outperforms baseline methods designed withtask-specific retrievers and also achieves state-of-the-art zero-shotperformance.

ControlRetriever: Harnessing the Power of Instructions for Controllable Retrieval