--- title: "Who cites From doc2query to docTTTTTquery — 89 citing papers" description: "89 papers of the Elamur Research library cite «From doc2query to docTTTTTquery», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/nogueira-2019-doc2query-docttttquery/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/nogueira-2019-doc2query-docttttquery/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [From doc2query to docTTTTTquery](https://md.research.elamur.ai/free/en/papers/nogueira-2019-doc2query-docttttquery/) / cited by # Who cites «From doc2query to docTTTTTquery» 89 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «From doc2query to docTTTTTquery»](https://md.research.elamur.ai/free/en/papers/nogueira-2019-doc2query-docttttquery/) 1. **[ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://md.research.elamur.ai/free/en/papers/khattab-2020-colbert-efficient-effective-passage/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Omar Khattab, Matei Zaharia*2020 · 1,789 citations* 2. **[RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/qu-2021-rocketqa-optimized-training-approach-dense/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yingqi Qu, Yuchen Ding, Jing Liu, et al.*2021 · 603 citations* 3. **[SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-sparse-lexical-expansion/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant*2021 · 512 citations* 4. **[Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling](https://md.research.elamur.ai/free/en/papers/hofstatter-2021-efficiently-teaching-effective-dense-retriever/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Sebastian Hofstatter, Sheng-Chieh Lin, Jheng-Hong Yang, et al.*2021 · 425 citations* 5. **[Query2doc: Query Expansion with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-query2doc-query-expansion-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liang Wang, Nan Yang, Furu Wei*2023 · 354 citations* 6. [**Text and Code Embeddings by Contrastive Pre-Training arXiv:2201.10005**Arvind Neelakantan, Tao Xu, Raul Puri, et al.*2022 · 323 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2022-text-code-embeddings-contrastive-pre/) 7. [**Large Dual Encoders Are Generalizable Retrievers arXiv:2112.07899**Jianmo Ni, Chen Qu, Jing Lu, et al.*2021 · 280 citations*](https://md.research.elamur.ai/free/en/papers/ni-2021-large-dual-encoders-generalizable-retrievers/) 8. [**BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models NeurIPS 2021 Datasets and Benchmarks**Nandan Thakur, Nils Reimers, Andreas Rücklé, et al.*2021 · 265 citations*](https://md.research.elamur.ai/free/en/papers/thakur-2021-beir-heterogenous-benchmark-zero/) 9. **[RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking](https://md.research.elamur.ai/free/en/papers/ren-2021-rocketqav2-joint-training-method-dense/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruiyang Ren, Yingqi Qu, Jing Liu, et al.*2021 · 256 citations* 10. **[GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval](https://md.research.elamur.ai/free/en/papers/wang-2022-gpl-generative-pseudo-labeling-unsupervised/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kexin Wang, Nandan Thakur, Nils Reimers, et al.*2022 · 155 citations* 11. **[SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval](https://md.research.elamur.ai/free/en/papers/wang-2023-simlm-pre-training-representation-bottleneck/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2023 · 106 citations* 12. [**SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval arXiv:2109.10086**Thibault Formal, Carlos Lassance, Benjamin Piwowarski, et al.*2021 · 83 citations*](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-v2-sparse-lexical/) 13. **[Distilling Large Embeddings via Hyperspherical Householder Quantization](https://md.research.elamur.ai/free/en/papers/wang-2026-distilling-large-embeddings-hyperspherical-householder/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihang Wang, Bin Wu, Yueyang Su, et al.*2026* 76 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/nogueira-2019-doc2query-docttttquery/) — From doc2query to docTTTTTquery