Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reimers, N., & Gurevych, I. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1410 · Cited by 12,778 · see citing works

Untitled work

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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 · see citing works

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 · see citing works

Learning deep structured semantic models for web search using clickthrough data

Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. P. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505665 · Cited by 2,054 · see citing works

ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Khattab, O., & Zaharia, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401075 · Cited by 1,301 · see citing works

MS MARCO: A Human Generated MAchine Reading COmprehension DatasetPreprint

Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T. G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.giscience.2025.5 · Cited by 1,190 · see citing works

A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

Shen, Y., He, X., Gao, J., Deng, L., & Mesnil, G. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2661935 · Cited by 673 · see citing works

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

Nguyen, T. G., Rosenberg, M., Xia, S., Gao, J., Tiwary, S., Majumder, R., & Deng, L. (2016) · neural information processing systems · Cited by 537 · see citing works

Document Ranking with a Pretrained Sequence-to-Sequence Model

Nogueira, R. F., Jiang, Z., Pradeep, R., & Lin, J. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.63 · Cited by 521 · see citing works

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 · see citing works

Sparse, Dense, and Attentional Representations for Text Retrieval

Luan, Y., Eisenstein, J., Toutanova, K., & Collins, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00369 · Cited by 363 · see citing works

Neural Ranking Models with Weak Supervision

Dehghani, M., Zamani, H., Severyn, A., Kamps, J., & Croft, W. B. (2017) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3077136.3080832 · Cited by 306 · see citing works

Context-Aware Sentence/Passage Term Importance Estimation For First Stage RetrievalPreprint

Dai, Z., & Callan, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.10687 · Cited by 171 · see citing works

RepBERT: Contextualized Text Embeddings for First-Stage RetrievalPreprint

Zhan, J., Mao, J., Liu, Y., Zhang, M., & Ma, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15498 · Cited by 121 · see citing works

A Dual Embedding Space Model for Document RankingPreprint

Mitra, B., Nalisnick, E., Craswell, N., & Caruana, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.01137 · Cited by 118 · see citing works

Learning to Reweight Terms with Distributed Representations

Zheng, G., & Callan, J. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767700 · Cited by 117 · see citing works

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 · see citing works

Pretrained Transformers for Text Ranking: BERT and BeyondPreprint

Lin, J., Nogueira, R. F., & Yates, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06467 · Cited by 101 · see citing works

Overview of the TREC 2019 deep learning track

Craswell, N., Mitra, B., Yilmaz, E., Campos, D., & Voorhees, E. M. (2020) · arXiv · doi:10.48550/arxiv.2003.07820 · Cited by 68 · see citing works

Untitled work

(2021) · doi:10.18653/v1/2021.naacl-tutorials.1 · Cited by 59 · see citing works

Modularized Transfomer-based Ranking Framework

Gao, L., Dai, Z., & Callan, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.342 · Cited by 51 · see citing works

DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling

Chen, J., Liu, Y., Raman, K., Bendersky, M., Yeh, J.-J., Zhou, Y., Najork, M., Cai, D., & Emadzadeh, E. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.264 · Cited by 28 · see citing works

Real-time Inference in Multi-sentence Tasks with Deep Pretrained Transformers.

Humeau, S., Shuster, K., Lachaux, M.-A., & Weston, J. (2019) · Cited by 21 · see citing works

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

Information Retrieval: Recent Advances and Beyond

Hambarde, K., & Proença, H. P. M. C. (2023) · IEEE Access · doi:10.1109/access.2023.3295776 · Cited by 176 · see citing works

Semantic Models for the First-Stage Retrieval: A Comprehensive Review

Guo, J., Cai, Y., Fan, Y., Sun, F., Zhang, R., & Cheng, X. (2022) · ACM Transactions on Information Systems · doi:10.1145/3486250 · Cited by 130 · see citing works

Pre-training Methods in Information Retrieval

Fan, Y., Xie, X., Cai, Y., Chen, J., Ma, X., Li, X., Zhang, R., & Guo, J. (2022) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000100 · Cited by 64 · see citing works

Multi-View Document Representation Learning for Open-Domain Dense Retrieval

Zhang, S., Liang, Y., Gong, M., Jiang, D., & Duan, N. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.414 · Cited by 45 · see citing works

Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval

Hong, W., Zhang, Z., Wang, J., & Zhao, H. Y. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.76 · Cited by 19 · see citing works

LIDER

Wang, Y., Ma, H., & Wang, D. Z. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3565816.3565819 · Cited by 16 · see citing works

Beyond Two-Tower: Attribute Guided Representation Learning for Candidate Retrieval

Shan, H., Zhang, Q., Liu, Z., Zhang, G., & Li, C. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583254 · Cited by 11 · see citing works

A Comprehensive Evaluation of Embedding Models and LLMs for IR and QA Across English and Italian

Oro, E., Granata, F. M., & Ruffolo, M. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9050141 · Cited by 10 · see citing works

Incorporating entity-level knowledge in pretrained language model for biomedical dense retrieval

Tan, J., Hu, J., & Dong, S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107535 · Cited by 10 · see citing works

I3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval

Dong, Q., Liu, Y., Ai, Q., Li, H., Wang, S., Liu, Y., Yin, D., & Ma, S. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614923 · Cited by 10 · see citing works

VIRT: Improving Representation-based Text Matching via Virtual Interaction

Li, D., Yang, Y., Tang, H., Liu, J., Wang, Q., Wang, J., Xu, T., Wu, W., & Chen, E. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.59 · Cited by 10 · see citing works

Incorporating Ranking Context for End-to-End BERT Re-ranking

Chen, X., Hui, K., He, B., Han, X., Sun, L., & Ye, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99736-6_8 · Cited by 9 · see citing works

On Elastic Language Models

Zhang, C., Wang, B., & Song, D. (2024) · ACM Transactions on Information Systems · doi:10.1145/3677375 · Cited by 8 · see citing works

GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval

Liu, J., Liu, J., Yang, Y., Wang, J., Wu, W., Zhao, D., & Yan, R. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.39 · Cited by 8 · see citing works

From Pre-Training to Meta-Learning: A Journey in Low-Resource-Language Representation Learning

Zaikis, D., & Vlahavas, I. P. (2023) · IEEE Access · doi:10.1109/access.2023.3326337 · Cited by 7 · see citing works

Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval

Jin, N., Li, D., Chen, J., Siebert, J. I., & Chen, Q. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-short.18 · Cited by 7 · see citing works

Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval

Ren, H., Shou, L., Wu, N., Gong, M., & Jiang, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.203 · Cited by 7 · see citing works

Sparse Pairwise Re-ranking with Pre-trained Transformers

Gienapp, L., Fröbe, M., Hagen, M., & Potthast, M. (2022) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3539813.3545140 · Cited by 6 · see citing works

DACL: A Domain-Adapted Contrastive Learning Approach to Low Resource Language Representations for Document Clustering Tasks

Zaikis, D., Kokkas, S., & Vlahavas, I. P. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-34204-2_47 · Cited by 4 · see citing works

Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval

Mekonnen, K. A., Tang, Y., & de Rijke, M. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730023 · Cited by 3 · see citing works

MIRS: [MASK] Insertion Based Retrieval Stabilizer for Query Variations

Liu, J., Gong, M., Hu, X., Yang, J., & Guo, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39847-6_31 · Cited by 2 · see citing works

Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval

Ren, H., Shou, L., Pei, J., Wu, N., Gong, M., & Jiang, D. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.31 · Cited by 2 · see citing works

Representation Decoupling for Open-Domain Passage Retrieval.Preprint

Wu, B., Zhang, Z., Wang, J., & Zhao, H. (2021) · arXiv (Cornell University) · Cited by 2 · see citing works

DACL+: domain-adapted contrastive learning for enhanced low-resource language representations in document clustering tasks

Zaikis, D., & Vlahavas, I. P. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10589-1 · Cited by 1 · see citing works

Multi-Step Semantic Reasoning in Generative Retrieval

Dong, S., Tang, Y., & de Rijke, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21300-6_17 · Cited by 1 · see citing works

CAMVR: Context-Adaptive Multi-View Representation Learning for Dense Retrieval

Liao, Z., Hou, X., Lou, D., Chen, Y., & Zhang, N. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10192020 · Cited by 1 · see citing works

Style-Driven Multi-Perspective Relevance Mining Model for Hotspot Reprint Paragraph Prediction

Wang, L., Qian, H., Li, Q., Xu, D. J., & Zeng, D. D. (2023) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi58743.2023.10297268 · Cited by 1 · see citing works

Dense Retrieval Systems with LLM-Based Query Expansion

Wang, Z., & Pei, Q. (2024) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat62293.2024.00110 · Cited by 1 · see citing works

Efficient Integration of ASR with Large Language Models to Enhance Video Search at Scale

Zhang, Q., Xiao, F., Li, T., Lin, L., Fang, H., Sun, H., Liu, R., Zhu, X., & Wang, J. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715220 · Cited by 1 · see citing works

Pseudo-Relevance for Enhancing Document Representation

Kim, J., Hwang, S.-W., Song, S. H., Ko, H., & Song, Y.-I. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.800 · Cited by 1 · see citing works

Translational Generative Retrieval via Potential Query Generation

Guo, Y., Liu, T., Sheng, J., Ma, D., Sun, M., & Tian, L. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888918

Optimizing Approximate Nearest Neighbour-Based Dense Retrieval for Efficient Passage Ranking with Query Expansion

(2025) · International journal of intelligent engineering and systems · doi:10.22266/ijies2025.0831.45

Dual-Encoder BERT Approach for Cross-Domain Scientific Literature Retrieval

Jovanović, M., Nikolić, J., Popović, A., & Đorđević, D. (2026) · Journal of Intelligent Information and Communication · doi:10.64972/jiic.2026v4.143p7s:80-93

An Improved Hybrid Approach using DBSCAN and Artificial Bee Colony Algorithm for Web Search Results Clustering

Saadan, M., Al-Maqaleh, B. M., & Al-Zeiadi, M. (2026) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta70636.2026.11652253

PQR: Improving Dense Retrieval via Potential Query Modeling

Kang, J., Li, R., Liu, Q., Chen, Y., Zhang, Z., Jiang, J., Yu, H., & Su, Y. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.660

SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval

Wang, L., Yang, N., Huang, X., Jiao, B., Yang, L., Jiang, D., Majumder, R., & Wei, F. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.125 · Cited by 71 · see citing works

Dense Retrieval on Large Language Models

Boreddy, V. N. R. (2024) · doi:10.13140/rg.2.2.13846.66886

ColBERT-PRF: Semantic Pseudo-Relevance Feedback for Dense Passage and Document Retrieval

Wang, X., Macdonald, C., Tonellotto, N., & Ounis, I. (2022) · ACM Transactions on the Web · doi:10.1145/3572405 · Cited by 45 · see citing works

Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings

Xu, L., Su, Z.-E., Yu, M., Li, J., Meng, F., & Zhou, J. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.1051 · Cited by 1 · see citing works