Sparse, Dense, and Attentional Representations for Text Retrieval
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Abstract Dual encoders perform retrieval by encoding documents and queries into dense low-dimensional vectors, scoring each document by its inner product with the query. We investigate the capacity of this architecture relative to sparse bag-of-words models and attentional neural networks. Using both theoretical and empirical analysis, we establish connections between the encoding dimension, the margin between gold and lower-ranked documents, and the document length, suggesting limitations in the capacity of fixed-length encodings to support precise retrieval of long documents. Building on these insights, we propose a simple neural model that combines the efficiency of dual encoders with some of the expressiveness of more costly attentional architectures, and explore sparse-dense hybrids to capitalize on the precision of sparse retrieval. These models outperform strong alternatives in large-scale retrieval.
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Two primary retrieval methods are suitable for non-parametric memory: sparse methods based on bag-of-words models and dense methods based on deep representation learning.
“Based on our review of existing work, we identify two primary retrieval methods suitable for non-parametric memory including sparse methods based on bag-of-words models and dense methods based on deep representation learning for vector encoding and relevance score calculation [269, 270, 232, 271].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [270]
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Zhang, H., Gong, Y., Shen, Y., Lv, J., Duan, N., & Chen, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.03611 · Cited by 61 · see citing works
Pre-trained Language Model for Web-scale Retrieval in Baidu Search
Liu, Y., Lu, W., Cheng, S., Shi, D., Wang, S., Cheng, Z., & Yin, D. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467149 · Cited by 60 · see citing works
Deep Cross-Modal Hashing Based on Semantic Consistent Ranking
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CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
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Self-citationThe Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes
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Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder
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Multi-Task Retrieval for Knowledge-Intensive Tasks
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CoSENT: Consistent Sentence Embedding via Similarity Ranking
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BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
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Multi-View Document Representation Learning for Open-Domain Dense Retrieval
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ColBERT-PRF: Semantic Pseudo-Relevance Feedback for Dense Passage and Document Retrieval
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Query Embedding Pruning for Dense Retrieval
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Understanding Hard Negatives in Noise Contrastive Estimation
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Dealing with Typos for BERT-based Passage Retrieval and Ranking
Zhuang, S., & Zuccon, G. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.225 · Cited by 42 · see citing works
AutoKG: Efficient Automated Knowledge Graph Generation for Language Models
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Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection
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Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval
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Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
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Phrase Retrieval Learns Passage Retrieval, Too
Lee, J., Wettig, A., & Chen, D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.297 · Cited by 38 · see citing works
On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval
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R2-D2: A Modular Baseline for Open-Domain Question Answering
Fajcik, M., Docekal, M., Ondrej, K., & Smrz, P. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.73 · Cited by 36 · see citing works
Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models
Chen, T., Zhang, M., Lu, J. R., Bendersky, M., & Najork, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99736-6_7 · Cited by 36 · see citing works
Learning to retrieve passages without supervision
Ram, O., Shachaf, G., Levy, O., Berant, J., & Globerson, A. (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.193 · Cited by 35 · see citing works
Questions Are All You Need to Train a Dense Passage Retriever
Sachan, D. S., Lewis, M., Yogatama, D., Zettlemoyer, L., Pineau, J., & Zaheer, M. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00564 · Cited by 35 · see citing works
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
Althammer, S., Hofstätter, S., Sertkan, M., Verberne, S., & Hanbury, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99736-6_2 · Cited by 34 · see citing works
A Confusion Method for the Protection of User Topic Privacy in Chinese Keyword-based Book Retrieval
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RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-rankingPreprint
Ren, R., Qu, Y., Liu, J., Zhao, W. X., She, Q., Wu, H. R., Wang, H., & Wen, J.-R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07367 · Cited by 34 · see citing works
A Memory Efficient Baseline for Open Domain Question AnsweringPreprint
Izacard, G., Petroni, F., Hosseini, L., Cao, N. D., Riedel, S., & Grave, É. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15156 · Cited by 33 · see citing works
Passage Retrieval for Outside-Knowledge Visual Question Answering
Qu, C., Zamani, H., Yang, L., Croft, W. B., & Miller, E. G. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462987 · Cited by 32 · see citing works
Generation-Augmented Retrieval for Open-domain Question Answering
Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., & Chen, W. (2020) · arXiv · doi:10.48550/arxiv.2009.08553 · Cited by 31 · see citing works
Uni-Retriever: Towards Learning the Unified Embedding Based Retriever in Bing Sponsored Search
Zhang, J., Liu, Z., Han, W., Xiao, S., Zheng, R., Shao, Y., Sun, H., Zhu, H., Srinivasan, P., Deng, W., et al. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539212 · Cited by 31 · see citing works
Introducing Neural Bag of Whole-Words with ColBERTer
Hofstätter, S., Khattab, O., Althammer, S., Sertkan, M., & Hanbury, A. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557367 · Cited by 31 · see citing works
Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and EfficientlyPreprint
Zhan, J., Mao, J., Liu, Y., Zhang, M., & Ma, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10469 · Cited by 30 · see citing works
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Tang, Y., Zhang, R., Guo, J., Chen, J., Zhu, Z., Wang, S., Yin, D., & Cheng, X. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599903 · Cited by 29 · see citing works
Scaling Laws For Dense Retrieval
Fang, Y., Zhan, J., Ai, Q., Mao, J., Su, W., Chen, J., & Liu, Y. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657743 · Cited by 28 · see citing works
More Robust Dense Retrieval with Contrastive Dual Learning
Li, Y., Liu, Z., Xiong, C., & Liu, Z. (2021) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3471158.3472245 · Cited by 28 · see citing works
Liu, J., Kang, Y., Tang, D., Song, K., Sun, C., Wang, X., Lu, W. P., & Liu, X. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560683 · Cited by 28 · see citing works
RetGen: A Joint Framework for Retrieval and Grounded Text Generation Modeling
Zhang, Y., Sun, S., Gao, X., Fang, Y., Brockett, C., Galley, M., Gao, J., & Dolan, B. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21429 · Cited by 27 · see citing works
Kong, W., Khadanga, S., Li, C., Gupta, S. K., Zhang, M., Xu, W., & Bendersky, M. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539137 · Cited by 27 · see citing works
Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks
Louis, A., DIJCK, G. V., & Spanakis, G. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.203 · Cited by 27 · see citing works
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Zhou, K., Gong, Y., Liu, X., Zhao, W. X., Shen, Y., Dong, A., Lü, J., Majumder, R., Wen, J.-R., & Duan, N. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-industry.56 · Cited by 27 · see citing works
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval
Tang, H., Sun, X., Jin, B., Wang, J., Zhang, F., & Wu, W. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.392 · Cited by 26 · see citing works
Challenges in Generalization in Open Domain Question Answering
Liu, L., Lewis, P., Riedel, S., & Stenetorp, P. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.155 · Cited by 26 · see citing works
A Free Format Legal Question Answering System
Khazaeli, S., Punuru, J. R., Morris, C., Sharma, S., Staub, B., Cole, M., Chiu-Webster, S., & Sakalley, D. (2021) · Natural Legal Language Processing Workshop (NLLP) · doi:10.18653/v1/2021.nllp-1.11 · Cited by 26 · see citing works
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
Chuang, Y.-S., Fang, W., Li, S.-W. D., Yih, W.-T., & Glass, J. R. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.768 · Cited by 26 · see citing works
Joint Passage Ranking for Diverse Multi-Answer Retrieval
Min, S., Lee, K., Chang, M.-W., Toutanova, K., & Hajishirzi, H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.560 · Cited by 26 · see citing works
Self-citationTowards Effective and Efficient Sparse Neural Information Retrieval
Formal, T., Lassance, C., Piwowarski, B., & Clinchant, S. (2023) · ACM Transactions on Information Systems · doi:10.1145/3634912 · Cited by 25 · see citing works
TravelRAG: A Tourist Attraction Retrieval Framework Based on Multi-Layer Knowledge Graph
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Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations
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LFR-GAN: Local Feature Refinement based Generative Adversarial Network for Text-to-Image Generation
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Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity
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A Dense Representation Framework for Lexical and Semantic Matching
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Retrieval And Structuring Augmented Generation with Large Language Models
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Persona-Based Conversational AI: State of the Art and Challenges
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Improving Wikipedia verifiability with AI
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Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings
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Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision
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Towards Query Performance Prediction for Neural Information Retrieval: Challenges and Opportunities
Faggioli, G., Formal, T., Lupart, S., Marchesin, S., Clinchant, S., Ferro, N., & Piwowarski, B. (2023) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3578337.3605142 · Cited by 21 · see citing works
- Authors
- Luan, Y., Eisenstein, J., Toutanova, K., & Collins, M.
- Affiliations
- Google (United States) (US)
- Corresponding author
- Luan, Yi
- Eisenstein, Jacob
- Toutanova, Kristina
- Collins, Michael
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2021
- Volume
- 9
- Pages
- 329-345
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W3157758108
- Cited by
- 363
- References
- 61
- Citations by year
- 2026: 46 · 2025: 48 · 2024: 39 · 2023: 81 · 2022: 69 · 2021: 61 · 2020: 19
- FWCI
- 56.52
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- 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
- text retrieval, dual-encoder models, sparse retrieval, bag-of-words model, document length, retrieval precision, large-scale retrieval
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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