Recent studies have shown that dense retrieval models, lacking dedicated training data, struggle to perform well across diverse retrieval tasks, as different retrieval tasks often entail distinct search intents. To address this challenge, in this work we introduce ControlRetriever, a generic and efficient approach with a parameter isolated architecture, capable of controlling dense retrieval models to directly perform varied retrieval tasks, harnessing the power of instructions that explicitly describe retrieval intents in natural language. Leveraging the foundation of ControlNet, which has proven powerful in text-to-image generation, ControlRetriever imbues different retrieval models with the new capacity of controllable retrieval, all while being guided by task-specific instructions. Furthermore, we propose a novel LLM guided Instruction Synthesizing and Iterative Training strategy, which iteratively tunes ControlRetriever based on extensive automatically-generated retrieval data with diverse instructions by capitalizing the advancement of large language models. Extensive experiments show that in the BEIR benchmark, with only natural language descriptions of specific retrieval intent for each task, ControlRetriever, as a unified multi-task retrieval system without task-specific tuning, significantly outperforms baseline methods designed with task-specific retrievers and also achieves state-of-the-art zero-shot performance.

翻译：近期研究表明，稠密检索模型因缺乏专用训练数据，在面对不同检索任务时往往表现欠佳，这是由于不同检索任务通常蕴含着差异化的搜索意图。为应对这一挑战，本文提出了ControlRetriever——一种采用参数隔离架构的通用高效方法，能够通过操控稠密检索模型直接执行多样化的检索任务，其核心在于利用自然语言中明确描述检索意图的指令之力。基于在文本到图像生成领域已证明强大效能的ControlNet框架，ControlRetriever为不同检索模型注入了可控检索的新能力，且全程由任务特定指令引导。此外，我们提出了一种新颖的大语言模型引导的指令合成与迭代训练策略，该策略通过利用大型语言模型的进步，基于大量自动生成的包含多样化指令的检索数据，对ControlRetriever进行迭代调优。大量实验表明，在BEIR基准测试中，仅需为每个任务提供特定检索意图的自然语言描述，ControlRetriever作为无需任务特定调优的统一多任务检索系统，其性能显著优于采用任务特定检索器的基线方法，并达到零样本学习的最优水平。