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We propose Referral-Augmented Retrieval (RAR), a simple technique thatconcatenates document indices with referrals, i.e. text from other documentsthat cite or link to the given document, to provide significant performancegains for zero-shot information retrieval. The key insight behind our method isthat referrals provide a more complete, multi-view representation of adocument, much like incoming page links in algorithms like PageRank provide acomprehensive idea of a webpage's importance. RAR works with both sparse anddense retrievers, and outperforms generative text expansion techniques such asDocT5Query and Query2Doc a 37% and 21% absolute improvement on ACL paperretrieval Recall@10 -- while also eliminating expensive model training andinference. We also analyze different methods for multi-referral aggregation andshow that RAR enables up-to-date information retrieval without re-training.

Referral Augmentation for Zero-Shot Information Retrieval