ControlRetriever: Harnessing the Power of Instructions for Controllable
  Retrieval
Kaihang Pan Juncheng Li Hongye Song Hao Fei Wei Ji Shuo Zhang Jun Lin Xiaozhong Liu Siliang Tang
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.