We study the problem of retrieval with instructions, where users provide explicit descriptions of their intent along with their queries to guide a retrieval system.
Our solution is a general-purpose task-aware retrieval system, trained using multi-task instruction tuning and can follow human-written instructions to find relevant documents to a given query.
We introduce the first large-scale collection of 37 retrieval datasets with instructions, BERRI, and present TART, a single multi-task retrieval system trained on BERRI with instructions that can adapt to a new task without any parameter updates.
TART 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 in which diverse domains and tasks are pooled.
TART significantly outperforms competitive baselines in this setup, further highlighting the effectiveness of guiding retrieval with instructions.