The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Unsupervised Cross-lingual Representation Learning at Scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.747 · Cited by 5,637
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
SimCSE: Simple Contrastive Learning of Sentence Embeddings
Gao, T., Yao, X., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.552 · Cited by 2,851
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069
Evaluating Large Language Models Trained on Code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436
Finetuned Language Models Are Zero-Shot Learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101
Training Compute-Optimal Large Language Models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2176 · Cited by 932
LaMDA: Language Models for Dialog Applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · arXiv · doi:10.48550/arxiv.2201.08239 · Cited by 778
Latent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., & Toutanova, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1612 · Cited by 771
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2110.08207 · Cited by 612
Self-citationQu, Y., Ding, Y., Liu, J., Liu, K., Ren, R., Zhao, W. X., Dong, D., Wu, H. R., & Wang, H. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.466 · Cited by 597
Holistic Evaluation of Language Models
Bommasani, R., Liang, P., & Lee, T. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15007 · Cited by 480
Fact or Fiction: Verifying Scientific Claims
Wadden, D., Lin, S., Lo, K., Wang, L. L., van Zuylen, M., Cohan, A., & Hajishirzi, H. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.609 · Cited by 382
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Xiong, L., Xiong, C., Li, Y., Tang, K., Liu, J., Bennett, P. N., Ahmed, J., & Overwikj, A. (2021) · International Conference on Learning Representations · Cited by 376
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
Hofstätter, S., Lin, S.-C., Yang, J.-H., Lin, J., & Hanbury, A. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462891 · Cited by 374
Self-citationLin, J., Ma, X., Lin, S.-C., Yang, J.-H., Pradeep, R., & Nogueira, R. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3463238 · Cited by 360
Self-citationCLIMATE-FEVER: A Dataset for Verification of Real-World Climate ClaimsPreprint
Diggelmann, T., Boyd-Graber, J. L., Bulian, J., Ciaramita, M., & Leippold, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00614 · Cited by 280
Condenser: a Pre-training Architecture for Dense Retrieval
Gao, L., & Callan, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.75 · Cited by 256
Self-citationUnsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
Gao, L., & Callan, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.203 · Cited by 251
Self-citationScaling Language Models: Methods, Analysis & Insights from Training Gopher
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.11446 · Cited by 235
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Su, H., Shi, W., Kasai, J., Wang, Y., Hu, Y., Mari Ostendorf, E., Yih, W.-T., Smith, N. A., Zettlemoyer, L., & Yu, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.71 · Cited by 190
Maia, M., Handschuh, S., Freitas, A., Davis, B., McDermott, R., Zarrouk, M., & Balahur, A. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3192301 · Cited by 178
(2022) · doi:10.18653/v1/2022.emnlp-main.669 · Cited by 177
Unsupervised Dense Information Retrieval with Contrastive Learning
Izacard, G., Caron, M., Hosseini, L., Riedel, S., Bojanowski, P., Joulin, A., & Grave, E. (2021) · arXiv · doi:10.48550/arxiv.2112.09118 · Cited by 172
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Lewis, P., Wu, Y., Liu, L., Minervini, P., Küttler, H., Piktus, A., Stenetorp, P., & Riedel, S. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00415 · Cited by 171
MetaICL: Learning to Learn In Context
Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.201 · Cited by 169
In-Batch Negatives for Knowledge Distillation with Tightly-Coupled Teachers for Dense Retrieval
Lin, S.-C., Yang, J.-H., & Lin, J. (2021) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2021.repl4nlp-1.17 · Cited by 156
Self-citationTransformer Memory as a Differentiable Search Index
Tay, Y., Tran, V., Dehghani, M., Ni, J., Bahri, D., Mehta, H., Qin, Z., Hui, K., Zhao, Z., Gupta, J., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1587 · Cited by 128
Rethinking search: making domain experts out of dilettantes
Metzler, D., Tay, Y., Bahri, D., & Najork, M. (2021) · ACM SIGIR Forum · doi:10.1145/3476415.3476428 · Cited by 112
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse LanguagesPreprint
Clark, J. H., Choi, E., Collins, M., Garrette, D., Kwiatkowski, T., Nikolaev, V., & Palomaki, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05002 · Cited by 109
InPars: Unsupervised Dataset Generation for Information Retrieval
Bonifacio, L., Abonizio, H., Fadaee, M., & Nogueira, R. F. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531863 · Cited by 108
Overview of the TREC 2020 Deep Learning Track
Craswell, N., Mitra, B., Yilmaz, E., & Campos, D. (2021) · arXiv · doi:10.48550/arxiv.2102.07662 · Cited by 107
Retrieval of the Best Counterargument without Prior Topic Knowledge
Wachsmuth, H., Syed, S., & Stein, B. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1023 · Cited by 106
Hasibi, F., Nikolaev, F., Xiong, C., Balog, K., Bratsberg, S. E., Kotov, A., & Callan, J. (2017) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3077136.3080751 · Cited by 105
Self-citationZero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation
Ma, J., Korotkov, I., Yang, Y., Hall, K., & McDonald, R. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.92 · Cited by 104
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
Zhang, X., Ma, X., Shi, P., & Lin, J. (2021) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2021.mrl-1.12 · Cited by 98
Self-citationImproving Passage Retrieval with Zero-Shot Question Generation
Sachan, D., Lewis, M., Joshi, M., Aghajanyan, A., Yih, W.-T., Pineau, J., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.249 · Cited by 90
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval
Wang, K., Thakur, N., Reimers, N., & Gurevych, I. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.168 · Cited by 83
Autoregressive Search Engines: Generating Substrings as Document Identifiers
Bevilacqua, M., Ottaviano, G., Lewis, P., Yih, S., Riedel, S., & Petroni, F. (2022) · neural information processing systems · doi:10.52202/068431-2296 · Cited by 80
TREC-COVID: Constructing a Pandemic Information Retrieval Test CollectionPreprint
Voorhees, E. M., Alam, T., Bedrick, S. D., Demner‐Fushman, D., Hersh, W., Lo, K., Roberts, K. E., Soboroff, I. M., & Wang, L. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.04474 · Cited by 68
Yu, Y., Xiong, C., Sun, S., Zhang, C., & Overwijk, A. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.95 · Cited by 58
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder
Lu, S., He, D., Xiong, C., Ke, G., Malik, W., Dou, Z., Bennett, P., Liu, T.-Y., & Overwijk, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.220 · Cited by 49
Task-aware Retrieval with Instructions
Asai, A., Schick, T., Lewis, P., Chen, X., Izacard, G., Riedel, S., Hajishirzi, H., & Yih, W.-T. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.225 · Cited by 46
Promptagator: Few-shot Dense Retrieval From 8 Examples
Dai, Z., Zhao, V. Y., Ma, J., Luan, Y., Ni, J., Lu, J., Bakalov, A., Guu, K., Hall, K. B., & Chang, M.-W. (2022) · arXiv · doi:10.48550/arxiv.2209.11755 · Cited by 43
Generative Multi-hop Retrieval
Lee, H., Yang, S., Oh, H., & Seo, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.92 · Cited by 26
TREC 2018 News Track Overview.
Soboroff, I. M., Huang, S., & Harman, D. K. (2018) · Text REtrieval Conference · Cited by 14
Research works printed but not linked: 5
- Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M. Voorhees. 2020. Overview of the TREC 2019 deep learning track. arXiv:2003.07820. · arXiv
- Zheng Liu and Yingxia Shao. 2022. RetroMAE: Pre- training retrieval-oriented transformers via masked auto-encoder. arXiv:2205.12035. · arXiv
- Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, and Yinfei Yang. 2022. Large dual encoders are generalizable retrievers. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9844-9855, Abu Dhabi, United Arab Emirates. As- sociation for Computational Linguistics.
- Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Tr- ishala Neeraj, Jos Rozen, Abheesht Sharma, An- drea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M. Rush. 2022. Multi- task prompted training enables zero-shot task gener- alization. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net.
- Nandan Thakur, Nils Reimers, Andreas Rücklé, Ab- hishek Srivastava, and Iryna Gurevych. 2021. BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. In Thirty-fifth Con- ference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
Cited by
Sorted by citations
Retrieval-Augmented Generation for Large Language Models: A Survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2023) · arXiv · doi:10.48550/arxiv.2312.10997 · Cited by 876
“HyDE constructs query vectors using textual cues to generate a “hypothetical” document capturing essential patterns [ Gao et al. , 2022 ] .”
MethodActive Retrieval Augmented Generation
Jiang, Z., Xu, F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., & Neubig, G. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.495 · Cited by 497
Self-citationMemory in the Age of AI Agents
Hu, Y., Liu, S., Yue, Y., Zhang, G., Liu, B., Zhu, F., Lin, J., Guo, H., Dou, S., Xi, Z., et al. (2025) · arXiv · doi:10.48550/arxiv.2512.13564 · Cited by 389
Self-citationIs ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
Sun, W., Yan, L., Ma, X., Wang, S., Ren, P., Chen, Z., Yin, D., & Ren, Z. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.923 · Cited by 270
Self-citationRetrieval augmented generation for large language models in healthcare: A systematic review
Amugongo, L. M., Mascheroni, P., Brooks, S., Doering, S., & Seidel, J. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000877 · Cited by 267
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 ›
Are you an author, or do you see a problem with this page? Request a review
