--- title: "Who cites Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval" description: "78 papers of the Elamur Research library cite «Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval», found in their bibliographies; the…" canonical: "https://research.elamur.ai/free/en/papers/zhang-2021-mr-tydi-multi-lingual-benchmark/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhang-2021-mr-tydi-multi-lingual-benchmark/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Mr. TyDi: A Multi-lingual Benchmark for Dense…](https://md.research.elamur.ai/free/en/papers/zhang-2021-mr-tydi-multi-lingual-benchmark/) / cited by # Who cites «Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval» 78 papers of our library cite it (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · [← back to «Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval»](https://md.research.elamur.ai/free/en/papers/zhang-2021-mr-tydi-multi-lingual-benchmark/) 1. [**M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation arXiv:2402.03216**Jianlv Chen, Shitao Xiao, Peitian Zhang, et al.*2024 · 1,196 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-m3-embedding-multi-linguality/) 2. [**Unsupervised Dense Information Retrieval with Contrastive Learning arXiv:2112.09118**Gautier Izacard, Mathilde Caron, Lucas Hosseini, et al.*2021 · 836 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/) 3. **[Precise Zero-Shot Dense Retrieval without Relevance Labels](https://md.research.elamur.ai/free/en/papers/gao-2022-precise-zero-shot-dense-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luyu Gao, Xueguang Ma, Jimmy Lin, et al.*2022 · 549 citations* 4. **[Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://md.research.elamur.ai/free/en/papers/sun-2023-chatgpt-good-search-investigating-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weiwei Sun, Lingyong Yan, Xinyu Ma, et al.*2023 · 496 citations* 5. **[Improving Text Embeddings with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-improving-text-embeddings-large-language/) [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.*2024 · 437 citations* 6. [**Multilingual E5 Text Embeddings: A Technical Report arXiv:2402.05672**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2024 · 346 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-multilingual-e5-text-embeddings-technical/) 7. [**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/) 8. **[mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xin Zhang, Yanzhao Zhang, Dingkun Long, et al.*2024 · 237 citations* 9. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models arXiv:2405.17428**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 222 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/) 10. [**LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders arXiv:2404.05961**Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, et al.*2024 · 213 citations*](https://md.research.elamur.ai/free/en/papers/behnamghader-2024-llm2vec-large-language-models-secretly/) 11. **[RealTime QA: What's the Answer Right Now?](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al.*2022 · 181 citations* 12. [**Generative Representational Instruction Tuning arXiv:2402.09906**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 172 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/) 13. **[MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages](https://md.research.elamur.ai/free/en/papers/zhang-2023-miracl-multilingual-retrieval-dataset-covering/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, et al.*2023 · 149 citations* 14. [**Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations arXiv:2303.18027**Jungo Kasai, Yuhei Kasai, Keisuke Sakaguchi, et al.*2023 · 77 citations*](https://md.research.elamur.ai/free/en/papers/kasai-2023-evaluating-gpt-4-chatgpt-japanese/) 15. [**Repetition Improves Language Model Embeddings arXiv:2402.15449**Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/springer-2024-repetition-improves-language-model-embeddings/) 16. [**Making Text Embedders Few-Shot Learners arXiv:2409.15700**Chaofan Li, MingHao Qin, Shitao Xiao, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-making-text-embedders-few-shot/) 17. **[Qur'an QA 2023 Shared Task: Overview of Passage Retrieval and Reading Comprehension Tasks over the Holy Qur'an](https://md.research.elamur.ai/free/en/papers/malhas-2023-qur-qa-2023-shared-task/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Rana Malhas, Watheq Mansour, Tamer Elsayed*2023 · 33 citations* 18. **[The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design](https://md.research.elamur.ai/free/en/papers/snegirev-2025-russian-focused-embedders-exploration-rumteb/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Artem Snegirev, Maria Tikhonova, Maksimova Anna, et al.*2025 · 19 citations* 19. **[Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks](https://md.research.elamur.ai/free/en/papers/bhatia-2025-swan-arabicmteb-dialect-aware-arabic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gagan Bhatia, El Moatez Billah Nagoudi, Abdellah El Mekki, et al.*2025 · 13 citations* 20. **[GYM at Qur'an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models](https://md.research.elamur.ai/free/en/papers/mahmoudi-2023-gym-qur-qa-2023-shared/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Ghazaleh Mahmoudi, Yeganeh Morshedzadeh, Sauleh Eetemadi*2023 · 7 citations* 21. **[AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages](https://md.research.elamur.ai/free/en/papers/uemura-2026-afrimteb-afrie5-benchmarking-adapting-text/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kosei Uemura, Miaoran Zhang, David Ifeoluwa Adelani*2026 · 4 citations* 22. **[When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval](https://md.research.elamur.ai/free/en/papers/zhu-2026-when-does-mixing-help-analyzing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tongyao Zhu, Huang Chao Ming, Min-Yen Kan*2026* 56 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhang-2021-mr-tydi-multi-lingual-benchmark/) — Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval