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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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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
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
Chen, Q., Zhou, W., Cheng, J., & Yang, J. X. (2024) · Applied Sciences · doi:10.3390/app142411529 · Cited by 7
Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-Agent LLMs
Li, X., Su, L., Jia, P., Cheng, S., Wang, J., Yin, D., & Zhao, X. (2025) · ACM Transactions on Information Systems · doi:10.1145/3749099 · Cited by 39
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
“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 6
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
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 10
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
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
On Technique Identification and Threat-Actor Attribution using LLMs and Embedding Models
Guru, K., Moss, R. J., & Kochenderfer, M. J. (2025) · International Conference on Cybersecurity and AI-Based Systems (Cyber-AI) · doi:10.1109/cyber-ai66431.2025.11233715 · Cited by 6
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
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
Incorporating Q&A Nuggets Into Retrieval-Augmented Generation
Dietz, L., Li, B., Liu, G. K.-M., Ju, J.-H., Yang, E., Lawrie, D., Gantt, W. H., & Mayfield, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21300-6_20 · Cited by 3
Query Expansion and Verification with Large Language Model for Information Retrieval
Zhang, W., Liu, Z., Wang, K., & Lian, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5672-8_29 · Cited by 3
Xinyu: An Efficient LLM-based System for Commentary Generation
Wu, Y., Tang, B., Xi, C., Yu, Y., Wang, P., Liu, Y., Kuang, K., Deng, H., Li, Z., Xiong, F., et al. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671537 · Cited by 3
Chen, A., & Tran, S. (2024) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds60720.2024.00025 · Cited by 4
R 2 LLMs: Retrieval and Ranking with LLMs
Zuccon, G., Zhuang, S., & Ma, X. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3731689 · Cited by 4
Self-citationMishra, P., Mahakali, A., & Venkataraman, P. S. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711642 · Cited by 5
LLM as HPC Expert: Extending RAG Architecture for HPC DataPreprint
Miyashita, Y., Tung, P. K. M., & Barthélemy, J. (2025) · Qeios · doi:10.32388/imkkvr · Cited by 7
BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents
Chen, Z., Ma, X., Zhuang, S., Nie, P., Zou, K., Sharifymoghaddam, S., Liu, A., Green, J., Patel, K., Meng, R., et al. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.1023 · Cited by 2
“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
Gong, X., He, R., & Peng, Q. (2024) · doi:10.1109/ccat64370.2024.00021 · Cited by 2
Chen, Q., & Ni, L. (2024) · International Conference on Electronic Information Engineering and Computer Communication (EIECC) · doi:10.1109/eiecc64539.2024.10929529 · Cited by 2
GraphRAG Makes it Possible to Digest Convoluted Legal Jargon
Ngangmeni, J., & Rawat, D. B. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00268 · Cited by 2
Remind: Recall Enhanced Memory Integration for Natural Language Dialogue Systems
Huang, B.-S., Kuo, T. T., Wang, L.-J., & Lin, C. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00125 · Cited by 2
Exploiting Query Reformulation and Reciprocal Rank Fusion in Math-Aware Search Engines
Kassaie, B., Kane, A., & Tompa, F. W. (2025) · ACM Symposium on Document Engineering (DocEng) · doi:10.1145/3704268.3742687 · Cited by 2
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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