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 often relied on document retrieval methods that introduced noise and excessive context.
“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]
The Introduction discusses dense retrieval and the difficulty of zero-shot retrieval, while the HyDE description discusses filtering hallucinated details from a generated hypothetical document. Neither passage addresses whether early RAG implementations often retrieved noisy documents or supplied excessive context; searches for those points found no supporting passage in our copy. (LLM, conf=0.76)
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Pu, Y., He, Z., Qiu, T., Wu, H., & Yu, B. (2024) · International Conference on Computer Aided Design · doi:10.1145/3676536.3676730 · Cited by 29 · see citing works
SciSciGPT: advancing human–AI collaboration in the science of science
Shao, E., Wang, Y., Qian, Y., Pan, Z., Liu, H., & Wang, D. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00906-6 · Cited by 26 · see citing works
KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG
Huang, Y., Zhang, S., & Xiao, X. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737012 · Cited by 23 · see citing works
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering
Rani, A., Tonmoy, S. M. T. I., Dalal, D., Gautam, S., Chakraborty, M., Chadha, A. R., Sheth, A., & Das, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.581 · Cited by 23 · see citing works
MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generation
Simoni, M., Saracino, A., Vinod, P., & Conti, M. (2025) · ACM Symposium on Applied Computing · doi:10.1145/3672608.3707898 · Cited by 16 · see citing works
Nayinzira, J. P., & Adda, M. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.11.118 · Cited by 15 · see citing works
Kozhipuram, A. V., Shailendra, S., & Kadel, R. (2025) · Information · doi:10.3390/info16090766 · Cited by 17 · see citing works
Retrieval Augmented Generation for Large Language Models in Healthcare: A Systematic ReviewPreprint
Amugongo, L. M., Mascheroni, P., Brooks, S. G., Doering, S., & Seidel, J. (2024) · Preprints.org · doi:10.20944/preprints202407.0876.v1 · Cited by 15 · see citing works
GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulation
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Domain-Driven LLM Development: Insights into RAG and Fine-Tuning Practices
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Large Search Model: Redefining Search Stack in the Era of LLMs
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"In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"
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LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration
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APEER : Automatic Prompt Engineering Enhances Large Language Model Reranking
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Prompt2Model: Generating Deployable Models from Natural Language Instructions
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A Retrieval-Augmented Generation Strategy to Enhance Medical Chatbot Reliability
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Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy
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From Missteps to Mastery: Enhancing Low-Resource Dense Retrieval through Adaptive Query Generation
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Enhancing Ultrahigh Resolution Remote Sensing Imagery Analysis With ImageRAG: A new framework
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SAGE: A Framework of Precise Retrieval for RAG
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Dense Retrieval Adaptation using Target Domain Description
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Data augmented large language models for medical record generation
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Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
Afzal, A., Vladika, J., Fazlija, G., Staradubets, A., & Matthes, F. (2024) · International Conference on Natural Language Processing and Information Retrieval (NLPIR) · doi:10.1145/3711542.3711575 · Cited by 9 · see citing works
Ranking Manipulation for Conversational Search Engines
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“For retrieval, important innovations include end-to-end retrieval fine-tuning (Lewis et al., 2020), query rewriting (Ma et al., 2023), and hypothetical document generation (Gao et al., 2023a).”
BackgroundKnowledge-Enhanced Conversational Agents
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RARoK:Retrieval-Augmented Reasoning on Knowledge for Medical Question Answering
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Context Awareness Gate for Retrieval Augmented Generation
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Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
Chen, G., Yu, W., Lu, X., Zhang, X., Meng, E., & Sha, L. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3622944 · Cited by 8 · see citing works
LLM-augmented multi-agent cooperative framework for medical case retrieval in cardiology
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Expand, Highlight, Generate: RL-driven Document Generation for Passage Reranking
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RAGVAL: Automatic Dataset Creation and Evaluation for RAG Systems
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Multilingual graph retrieval-augmented generation for product design using design knowledge
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Large language model augmented exercise retrieval for personalized language learning
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FolkRAG: a retrieval-augmented generation system for cultural heritage materials
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Ten Natural Language Processing Tasks with Generative Artificial Intelligence
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RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation
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Do Large Language Models Favor Recent Content? A Study on Recency Bias in LLM-Based Reranking
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Augmenting Zero-Shot Dense Retrievers with Plug-in Mixture-of-Memories
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Ifqa-llm: intelligent intention-driven financial question-answering with large language models
Chen, F., Huang, Y., Wang, J., Yu, C., & Meng, X. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07726-5 · Cited by 5 · see citing works
Integração de Modelos de Linguagem e RAG na Criação de Chatbots Oftalmológicos
Passinato, E., Rios, W. S. R., & Filho, A. R. G. (2024) · doi:10.5753/sbcas.2024.2228 · Cited by 5 · see citing works
SCodeSearcher: soft contrastive learning for code search
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Ye, Z., Qi, D., Liu, H., & Zhang, S. (2025) · Electronics · doi:10.3390/electronics14163284 · Cited by 5 · see citing works
Chakraborty, M., Pahwa, K., Rani, A., Chatterjee, S., Dalal, D., Dave, H., Garimella, R., Gurumurthy, P., Mahor, A., Mukherjee, S., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.945 · Cited by 6 · see citing works
QAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval
Tan, H., Zhan, S., Lin, H., Zheng, H.-T., & Chan, W. K. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3543203 · Cited by 7 · see citing works
Integrating External Knowledge with LLMs: A Systematic Review of RAG Approaches
Mikulić, I., Vlaić, M., Delač, G., Šilić, M., & Vladimir, K. (2025) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro65660.2025.11131735 · Cited by 4 · see citing works
Graded Relevance Scoring of Written Essays with Dense Retrieval
Albatarni, S., Eltanbouly, S., & Elsayed, T. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657744 · Cited by 4 · see citing works
Chen, Q., Zhou, W., Cheng, J., & Yang, J. X. (2024) · Applied Sciences · doi:10.3390/app142411529 · Cited by 4 · see citing works
Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-Agent LLMs
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Human-inspired Perspectives: A Survey on AI Long-term Memory
He, Z., Lin, W., Zheng, H., Zhang, F., Jones, M. W., Aitchison, L., Xu, X., Liu, M., Kristensson, P. O., & Shen, J. (2024) · arXiv · doi:10.48550/arxiv.2411.00489 · Cited by 3 · see citing works
“It also helps resolve conflicts and hallucinations in retrieved memory [145, 151] and can enhance query through techniques like LLM-based augmentation [203, 141, 205, 182, 208, 204, 207, 206] and reinforcement learning [19, 201, 202].”
LLM-Based Text Style Transfer: Have We Taken a Step Forward?
Toshevska, M., & Gievska, S. (2025) · IEEE Access · doi:10.1109/access.2025.3548967 · Cited by 4 · see citing works
PILs of Knowledge: A Synthetic Benchmark for Evaluating Question Answering Systems in Healthcare
Lunardi, R., Soprano, M., Coppola, P., Mea, V. D., Mizzaro, S., & Roitero, K. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730283 · Cited by 3 · see citing works
Constrained Auto-Regressive Decoding Constrains Generative Retrieval
Wu, S., Ren, Z., Xin, X., Yang, J., Zhang, M., Chen, Z., de Rijke, M., & Ren, P. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729934 · Cited by 3 · see citing works
MAAQR: An LLM-based Multi-Agent Framework for Adaptive Query Rewriting in Alipay Search
Zheng, Q., Zhong, M., Gong, S., Jiang, H., Wu, K., Liu, H., Xu, J., & Mo, L. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3731950 · Cited by 3 · see citing works
OPT2CODE: A retrieval-augmented framework for solving linear programming problems
Ahmed, T., & Choudhury, S. (2025) · Natural Language Processing Journal · doi:10.1016/j.nlp.2025.100185 · Cited by 5 · see citing works
On Technique Identification and Threat-Actor Attribution using LLMs and Embedding Models
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Wang, C., Chai, S., Xu, T., Adil, M., & Qiu, T. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3652422 · Cited by 4 · see citing works
Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption
Ye, H., Guo, J., Liu, Z., & Lam, K. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11391120 · Cited by 3 · see citing works
Incorporating Q&A Nuggets Into Retrieval-Augmented Generation
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Query Expansion and Verification with Large Language Model for Information Retrieval
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Xinyu: An Efficient LLM-based System for Commentary Generation
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R 2 LLMs: Retrieval and Ranking with LLMs
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LLM as HPC Expert: Extending RAG Architecture for HPC DataPreprint
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BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents
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“This concern has been raised since 2022, when Gao et al. (2022) asked “Will a weak retriever theoretically suffice as the NLU and NLG models rapidly become stronger?””
Self-citationReordering of Double Pass Merging Chunking to Improve Retrieval Augmented Generation
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GraphRAG Makes it Possible to Digest Convoluted Legal Jargon
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Remind: Recall Enhanced Memory Integration for Natural Language Dialogue Systems
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Exploiting Query Reformulation and Reciprocal Rank Fusion in Math-Aware Search Engines
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Big Loop and Atomization: A Holistic Review on the Expansion Capabilities of Large Language Models
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- 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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