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        <title>Referral Augmentation for Zero-Shot Information Retrieval</title>
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        <namePart type="given">Michael</namePart>
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        <namePart type="given">Karthik</namePart>
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    <abstract>We propose Referral-Augmented Retrieval (RAR), a simple technique that concatenates document indices with referrals: text from other documents that cite or link to the given document. We find that RAR provides significant performance gains for tasks across paper retrieval, entity retrieval, and open-domain question-answering in both zero-shot and in-domain (e.g., fine-tuned) settings. We examine how RAR provides especially strong improvements on more structured tasks, and can greatly outperform generative text expansion techniques such as DocT5Query and Query2Doc, with a 37% and 21% absolute improvement on ACL paper retrieval, respectively. We also compare three ways to aggregate referrals for RAR. Overall, we believe RAR can help revive and re-contextualize the classic information retrieval idea of using anchor texts to improve the representations of documents in a wide variety of corpuses in the age of neural retrieval.</abstract>
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%0 Conference Proceedings
%T Referral Augmentation for Zero-Shot Information Retrieval
%A Tang, Michael
%A Yao, Shunyu
%A Yang, John
%A Narasimhan, Karthik
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F tang-etal-2024-referral
%X We propose Referral-Augmented Retrieval (RAR), a simple technique that concatenates document indices with referrals: text from other documents that cite or link to the given document. We find that RAR provides significant performance gains for tasks across paper retrieval, entity retrieval, and open-domain question-answering in both zero-shot and in-domain (e.g., fine-tuned) settings. We examine how RAR provides especially strong improvements on more structured tasks, and can greatly outperform generative text expansion techniques such as DocT5Query and Query2Doc, with a 37% and 21% absolute improvement on ACL paper retrieval, respectively. We also compare three ways to aggregate referrals for RAR. Overall, we believe RAR can help revive and re-contextualize the classic information retrieval idea of using anchor texts to improve the representations of documents in a wide variety of corpuses in the age of neural retrieval.
%U https://aclanthology.org/2024.findings-acl.798
%P 13452-13461

Markdown (Informal)

[Referral Augmentation for Zero-Shot Information Retrieval](https://aclanthology.org/2024.findings-acl.798) (Tang et al., Findings 2024)

• Referral Augmentation for Zero-Shot Information Retrieval (Tang et al., Findings 2024)

ACL

• Michael Tang, Shunyu Yao, John Yang, and Karthik Narasimhan. 2024. Referral Augmentation for Zero-Shot Information Retrieval. In Findings of the Association for Computational Linguistics ACL 2024, pages 13452–13461, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.