Instruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts. In this work, we present Promptriever, the first retrieval model able to be prompted like an LM. To train Promptriever, we curate and release a new instance-level instruction training set from MS MARCO, spanning nearly 500k instances. Promptriever not only achieves strong performance on standard retrieval tasks, but also follows instructions. We observe: (1) large gains (reaching SoTA) on following detailed relevance instructions (+14.3 p-MRR / +3.1 nDCG on FollowIR), (2) significantly increased robustness to lexical choices/phrasing in the query+instruction (+12.9 Robustness@10 on InstructIR), and (3) the ability to perform hyperparameter search via prompting to reliably improve retrieval performance (+1.4 average increase on BEIR). Promptriever demonstrates that retrieval models can be controlled with prompts on a per-query basis, setting the stage for future work aligning LM prompting techniques with information retrieval.

本研究解决了传统检索模型无法接受指令提示的空白，提出了Promptriever，这是首个能够像语言模型一样响应指令的检索模型。研究发现，Promptriever在标准检索任务上表现优异，且在遵循详细的相关性指令方面显著提升，同时增强了对查询和指令的措辞选择的鲁棒性，展现了检索模型在每个查询中受控于提示的潜力。

Promptriever：可像语言模型一样被提示的指令训练检索模型