New retrieval tasks have always been emerging, thus urging the development of new retrieval models. However, instantiating a retrieval model for each new retrieval task is resource-intensive and time-consuming, especially for a retrieval model that employs a large-scale pre-trained language model. To address this issue, we shift to a novel retrieval paradigm called modular retrieval, which aims to solve new retrieval tasks by instead composing multiple existing retrieval modules. Built upon the paradigm, we propose a retrieval model with modular prompt tuning named REMOP. It constructs retrieval modules subject to task attributes with deep prompt tuning, and yields retrieval models subject to tasks with module composition. We validate that, REMOP inherently with modularity not only has appealing generalizability and interpretability in preliminary explorations, but also achieves comparable performance to state-of-the-art retrieval models on a zero-shot retrieval benchmark.\footnote{Our code is available at \url{https://github.com/FreedomIntelligence/REMOP}}

翻译：新检索任务不断涌现，促使新型检索模型的研发。然而，为每个新检索任务单独实例化一个检索模型（尤其是采用大规模预训练语言模型的检索模型）既耗费资源又耗时。为解决此问题，我们转向一种名为"模块化检索"的新范式，旨在通过组合多个现有检索模块来应对新检索任务。基于此范式，我们提出一种采用模块化提示调优的检索模型REMOP。该模型通过深度提示调优构建面向任务属性的检索模块，并通过模块组合得到面向任务的检索模型。我们验证发现，具有模块化特性的REMOP不仅在初步探索中展现出优异的泛化性和可解释性，还在零样本检索基准上达到了与最先进检索模型相媲美的性能。\footnote{我们的代码开源在 \url{https://github.com/FreedomIntelligence/REMOP}}