  We propose Referral-Augmented Retrieval (RAR), a simple technique that
concatenates document indices with referrals, i.e. text from other documents
that cite or link to the given document, to provide significant performance
gains for zero-shot information retrieval. The key insight behind our method is
that referrals provide a more complete, multi-view representation of a
document, much like incoming page links in algorithms like PageRank provide a
comprehensive idea of a webpage's importance. RAR works with both sparse and
dense retrievers, and outperforms generative text expansion techniques such as
DocT5Query and Query2Doc a 37% and 21% absolute improvement on ACL paper
retrieval Recall@10 -- while also eliminating expensive model training and
inference. We also analyze different methods for multi-referral aggregation and
show that RAR enables up-to-date information retrieval without re-training.
