Cited by 369Information Retrieval and Search BehaviorTopic ModelingAdvanced Image and Video Retrieval Techniques

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Precise Zero-Shot Dense Retrieval without Relevance Labels

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Gao, Luyu, Ma, Xueguang, Lin, Jimmy, Callan, Jamie·2023·Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

DOI 10.18653/v1/2023.acl-long.99

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Abstract

While dense retrieval has been shown to be effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance labels are available.In this paper, we recognize the difficulty of zero-shot learning and encoding relevance.Instead, we propose to pivot through Hypothetical Document Embeddings (HyDE).Given a query, HyDE first zero-shot prompts an instruction-following language model (e.g., InstructGPT) to generate a hypothetical document.The document captures relevance patterns but is "fake" and may contain hallucinations.Then, an unsupervised contrastively learned encoder (e.g., Contriever) encodes the document into an embedding vector.This vector identifies a neighborhood in the corpus embedding space, from which similar real documents are retrieved based on vector similarity.This second step grounds the generated document to the actual corpus, with the encoder's dense bottleneck filtering out the hallucinations.Our experiments show that HyDE significantly outperforms the state-ofthe-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers across various tasks (e.g.web search, QA, fact verification) and in non-English languages (e.g., sw, ko, ja, bn). 1

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Authors: Gao, L., Ma, X., Lin, J., & Callan, J.
Affiliations: Carnegie Mellon University (US)
University of Waterloo (CA)
ORCID: Gao, Luyu
Ma, Xueguang
Lin, Jimmy
Callan, Jamie
Venue: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher: Association for Computational Linguistics
Published: 2023
Pages: 1762-1777
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/2023.acl-long.99
OpenAlex: W4385565351
Cited by: 362
References: 53
Citations by year: 2026: 167 · 2025: 126 · 2024: 52 · 2023: 17
FWCI: 93.04
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Natural Sciences and Engineering Research Council of Canada
Topics: Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords: unsupervised contrastive learning, multilingual information retrieval, web search, question answering, fact-checking

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Precise Zero-Shot Dense Retrieval without Relevance Labels

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