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In this work, we introduce the Qwen3 Embedding series, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon the Qwen3 foundation models.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych · 2019
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Embedding-based retrieval in facebook search
Jui-Ting Huang, Ashish Sharma, Shuying Sun, Li Xia, David Zhang, Philip Pronin, Janani Padmanabhan, Giuseppe Ottaviano, and Linjun Yang · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick SH Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
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Text embeddings by weakly-supervised contrastive pre-training, 2022
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei · 2022
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Towards general text embeddings with multi-stage contrastive learning, 2023
Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang · 2023
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Zero-shot listwise document reranking with a large language model
Xueguang Ma, Xinyu Zhang, Ronak Pradeep, and Jimmy Lin · 2023
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MTEB: Massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loic Magne, and Nils Reimers · 2023
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Rankvicuna: Zero-shot listwise document reranking with open-source large language models
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin · 2023
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One embedder, any task: Instruction-finetuned text embeddings
Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A Smith, Luke Zettlemoyer, and Tao Yu · 2023
Cited alongside, same era.
Embedding in recommender systems: A survey
Xiangyu Zhao, Maolin Wang, Xinjian Zhao, Jiansheng Li, Shucheng Zhou, Dawei Yin, Qing Li, Jiliang Tang, and Ruocheng Guo · 2023
Cited alongside, same era.
M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation
Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu · 2024
Cited alongside, same era.
Scaling synthetic data creation with 1,000,000,000 personas
Tao Ge, Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, and Dong Yu · 2024
Cited alongside, same era.
C-pack: Packed resources for general chinese embeddings
Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie · 2024
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A two-stage adaptation of large language models for text ranking
Longhui Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, and Min Zhang · 2024
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mGTE: Generalized long-context text representation and reranking models for multilingual text retrieval
Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, and Min Zhang · 2024
Later among the works it cites.
Dense text retrieval based on pretrained language models: A survey
Wayne Xin Zhao, Jing Liu, Ruiyang Ren, and Ji-Rong Wen · 2024
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A setwise approach for effective and highly efficient zero-shot ranking with large language models
Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, and Guido Zuccon · 2024
Later among the works it cites.
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Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al · 2024
Cited alongside, same era.
Nv-embed: Improved techniques for training llms as generalist embedding models
Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping · 2024
Cited alongside, same era.
Mingxin Li, Zhijie Nie, Yanzhao Zhang, Dingkun Long, Richong Zhang, and Pengjun Xie · 2024
Cited alongside, same era.
Improving text embeddings with large language models
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei · 2024
Cited alongside, same era.
Followir: Evaluating and teaching information retrieval models to follow instructions
Orion Weller, Benjamin Chang, Sean MacAvaney, Kyle Lo, Arman Cohan, Benjamin Van Durme, Dawn Lawrie, and Luca Soldaini · 2024
Cited alongside, same era.
NV-embed: Improved techniques for training LLMs as generalist embedding models
Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping
Cited in the paper.
Gemini embedding: Generalizable embeddings from gemini
Jinhyuk Lee, Feiyang Chen, Sahil Dua, Daniel Cer, Madhuri Shanbhogue, Iftekhar Naim, Gustavo Hernández Ábrego, Zhe Li, Kaifeng Chen, Henrique Schechter Vera, et al
Cited in the paper.
MMTEB: Massive multilingual text embedding benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, Márton Kardos, Ashwin Mathur, David Stap, Jay Gala, Wissam Siblini, Dominik Krzemiński, Genta Indra Winata, et al · 2025
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Generative representational instruction tuning
Niklas Muennighoff, Hongjin SU, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, and Douwe Kiela · 2025
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An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al · 2025
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