  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.
