Referral Augmentation for Zero-Shot Information Retrieval

• URL: http://arxiv.org/abs/2305.15098v1
• Date: Wed, 24 May 2023 12:28:35 GMT
• Title: Referral Augmentation for Zero-Shot Information Retrieval
• Authors: Michael Tang, Shunyu Yao, John Yang, Karthik Narasimhan
• Abstract summary: Referral-Augmented Retrieval (RAR) is a simple technique that links document indices with referrals. RAR works with both sparse and dense retrievers, and outperforms generative text expansion techniques. We analyze different methods for multi-referral aggregation and show that enables up-to-date information retrieval without re-training.
• Score: 30.811093210831018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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