Modern Large Language Models (LLMs)  are capable of following long and complex instructions that enable a diverse amount of user tasks.
However, despite Information Retrieval (IR)  models using LLMs as the backbone of their architectures, nearly all of them still only take queries as input, with no instructions.
For the handful of recent models that do take instructions, it’s unclear how they use them.
We introduce our datasetFollowIR, which contains a rigorous instruction evaluation benchmark as well as a training set for helping IR models learn to better follow real-world instructions.FollowIRbuilds off the long history of the TREC conferences: as TREC provides human annotators with instructions (also known asnarratives)  to determine document relevance, so should IR models be able to understand and decide relevance based on these detailed instructions.
Our evaluation benchmark starts with three deeply judged TREC collections and alters the annotator instructions, re-annotating relevant documents.
Through this process, we can measure how well IR models follow instructions, through a new pairwise evaluation framework.
Our results indicate that existing retrieval models fail to correctly use instructions, using them for basic keywords and struggling to understand long-form information.
However, we show that it is possible for IR models to learn to follow complex instructions: our newFollowIR-7B model has significant improvements (over 13%)  after fine-tuning on our training set.