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Precise Zero-Shot Dense Retrieval without Relevance Labels
Open accessAbstract
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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“Early RAG implementations, however, often relied on document retrieval methods that introduced noise and excessive context [26, 55, 60, 122].”
From LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration (2025) · cites this paper as [26]
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Self-citationPrecise Zero-Shot Pointwise Ranking with LLMs through Post-Aggregated Global Context Information
Long, K., Li, S., Xu, C., Tang, J., & Wang, T. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730061
Zhang, B., Wang, S., Jiang, Y., Sui, D., Tu, Z., & Chu, D. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729898
Task-Classifying Retrieval Augmented Generation
Ju, J., Zang, P., Zhong, H., & Wang, L. (2025) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd64986.2025.11082008 · Cited by 1
NeuRAG: Retrieval-Augmented Generation based on Dynamic Neural Matching
Li, M., Song, S., Gao, N., & Zhang, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9894-3_19
P-LRR: A LLM-Enhanced Retrieval and Reranking Framework for Policy Domain
Qi, G., Yi, N., Wang, S., Chang, W., & Yu, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9921-6_38
Automated Question-Answer Generation for Evaluating Vietnamese RAG-Based Approaches
Nguyen, D.-H., & Nguyen, L. H. B. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-98164-7_21
Initial Evaluation of Retrieval-Augmented Generation Approaches in Spectrum Policy Research
Lu, B., Reinking, C., & Laneman, J. N. (2025) · IEEE International Symposium on Dynamic Spectrum Access Networks · doi:10.1109/dyspan64764.2025.11115912
Applying embedding models and advanced RAG techniques for Korean text classification
Kim, H. R., Noh, Y., Park, J.-H., Park, M., Yang, B., & Jung, Y. (2025) · Korean Journal of Applied Statistics · doi:10.5351/kjas.2025.38.4.571
A Media Content Recommendation Method for Playlist Curators using LLM-Based Query Expansion
Hagio, Y., Yamamura, C., Ogawa, H., Ohmata, H., & Fujii, A. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3748129
Xu, X., Chu, B., Lin, Q., Zhong, Y., Wen, F., Liu, J., Fei, B., Li, Y., Yang, Z., & Zhou, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06109-6_24
Code Retrieval with Mixture of Experts Prototype Learning Based on Classification
Ling, F., Huang, G., Wang, J., Yuan, X., Chen, X., Zhang, X. W., Zhang, F., & Pun, C. (2025) · Asia-Pacific Symposium on Internetware · doi:10.1145/3755881.3755893
Practical Decisions for Development of Retrieval-Augmented Generation Systems
Deksne, D., & Pinnis, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07986-2_8
Podstawski, M., & Кamola, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-03711-4_12
Semantic Embeddings for Literature Discovery: An AI Based Alternative to Keyword-Based SearchPreprint
Saknini, M., Hees, J., Jovanović, M., & Baaden, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5716887
Switch, Reason, and Revise: Enhancing Reasoning Capability of Video Game AI by Large Language Models
Li, W., Liu, H., Lv, J., Huang, K., Song, A., & Lei, Z. (2025) · IEEE Transactions on Games · doi:10.1109/tg.2025.3630322
Development of Autonomous Failure Maintenance System for Semiconductor Manufacturing
Han, N., Jiyeong, S. E. O., Ha, J., Oh, J., Lee, J., Jeong, B., Park, J., Kim, G. H., & Joo, Y. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761556 · Cited by 1
CLAP: Coreference-Linked Augmentation for Passage Retrieval
Xu, H., Xu, L., & Yuan, L. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761113
KUG: Joint Enhancement of Internal and External Knowledge for Retrieval-Augmented Generation
Li, M., Chen, S., Tu, S., Du, Z., Zhang, J., Li, Z., & Xiao, Y. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761414 · Cited by 1
Query, Decompose, Compress: Structured Query Expansion for Efficient Multi-Hop Retrieval
Yun, J., & Kim, Y. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760968 · Cited by 1
Zhang, J., Chen, Y., & Xu, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3349-7_1
Metadata
- Authors
- Gao, L., Ma, X., Lin, J., & Callan, J.
- Affiliations
- Carnegie Mellon University (US)
- University of Waterloo (CA)
- Venue
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2023
- Pages
- 1762-1777
- ISSN
- 0736-587X (ISSN-L)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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