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.- Anthology ID:
- 2024.findings-acl.798
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13452–13461
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-acl.798/
- DOI:
- 10.18653/v1/2024.findings-acl.798
- Cite (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. Association for Computational Linguistics.
- Cite (Informal):
- Referral Augmentation for Zero-Shot Information Retrieval (Tang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-acl.798.pdf