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.

使用控制器架构的 ControlRetriever，通过自然语言中提供的特定检索意图，解决了密集检索模型在不同检索任务中表现不佳的问题，同时结合大型语言模型的发展，提出了一种新的策略来迭代地训练 ControlRetriever，经过大量实验证明其在多个检索任务上显著优于基线方法，并取得了最先进的零 - shot 性能。