Abstract

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, $\mathbb{BERRI}$ , and present $\mathbb{TART}$ , a multi-task retrieval system trained on $\mathbb{BERRI}$ with instructions. $\mathbb{TART}$ 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, $\mathbb{X}^{2}$ -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, $\mathbb{TART}$ significantly outperforms competitive baselines, further demonstrating the effectiveness of guiding retrieval with instructions.11 1 Code, data and pretrain