We study the problem of retrieval with instructions, where users of a
retrieval system explicitly describe their intent along with their queries. We
aim to develop a general-purpose task-aware retrieval system using multi-task
instruction tuning, which can follow human-written instructions to find the
best documents for a given query. We introduce the first large-scale collection
of approximately 40 retrieval datasets with instructions, BERRI, and present
TART, a multi-task retrieval system trained on BERRI with instructions. TART
shows strong capabilities to adapt to a new retrieval task via instructions and
advances the state of the art on two zero-shot retrieval benchmarks, BEIR and
LOTTE, outperforming models up to three times larger. We further introduce a
new evaluation setup, X^2-Retrieval to better reflect real-world scenarios,
where diverse domains and tasks are pooled and a system needs to find documents
aligning users' intents. In this setup, TART significantly outperforms
competitive baselines, further demonstrating the effectiveness of guiding
retrieval with instructions.

本文介绍如何使用多任务指令调整来开发一般用途基于任务感知的检索系统，并且引入了包含 40 个使用指令的检索数据集 BERRI 和在其上使用指令训练的多任务检索系统 TART。此外，本文还引入了一个新的评估环境 X ^ 2-Retrieval，其中 TART 在广泛领域和任务汇集的情况下，在用户意图对齐的情况下显著优于竞争基线。