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A new trend uses LLMs as dense text encoders via contrastive learning.
Semeval-2012 task 6: A pilot on semantic textual similarity
Eneko Agirre, Daniel M. Cer, Mona T. Diab, and Aitor Gonzalez-Agirre. 2012 · 2012
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*sem 2013 shared task: Semantic textual similarity
Eneko Agirre, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, and Weiwei Guo. 2013 · 2013
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Semeval-2014 task 10: Multilingual semantic textual similarity
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014 · 2014
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Semeval-2015 task 2: Semantic textual similarity, english, spanish and pilot on interpretability
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, and Janyce Wiebe. 2015 · 2015
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Semeval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation
Eneko Agirre, Carmen Banea, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2016 · 2016
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Semeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel M. Cer, Mona T. Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick S. H. Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020 · 2020
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On the sentence embeddings from pre-trained language models
Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, and Lei Li. 2020 · 2020
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Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020 · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola. 2020 · 2020
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Deberta: decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
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Whitening sentence representations for better semantics and faster retrieval
Jianlin Su, Jiarun Cao, Weijie Liu, and Yangyiwen Ou. 2021 · 2021
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Semeval-2022 task 8: Multilingual news article similarity
Xi Chen, Ali Zeynali, Chico Q. Camargo, Fabian Flöck, Devin Gaffney, Przemyslaw A. Grabowicz, Scott Hale, David Jurgens, and Mattia Samory. 2022 · 2022
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IRRGN: an implicit relational reasoning graph network for multi-turn response selection
Jingcheng Deng, Hengwei Dai, Xuewei Guo, Yuanchen Ju, and Wei Peng. 2022 · 2022
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, and Yinfei Yang. 2022 · 2022
Cited alongside, same era.
Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022
Cited alongside, same era.
Task-aware retrieval with instructions
Akari Asai, Timo Schick, Patrick S. H. Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, and Wen-tau Yih. 2023 · 2023
Cited alongside, same era.
Scaling sentence embeddings with large language models
Ting Jiang, Shaohan Huang, Zhongzhi Luan, Deqing Wang, and Fuzhen Zhuang. 2024 · 2024
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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 · 2024
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Meta-task prompting elicits embeddings from large language models
Yibin Lei, Di Wu, Tianyi Zhou, Tao Shen, Yu Cao, Chongyang Tao, and Andrew Yates. 2024 · 2024
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Llama2vec: Unsupervised adaptation of large language models for dense retrieval
Chaofan Li, Zheng Liu, Shitao Xiao, Yingxia Shao, and Defu Lian. 2024a · 2024
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Meaning representations from trajectories in autoregressive models
Tian Yu Liu, Matthew Trager, Alessandro Achille, Pramuditha Perera, Luca Zancato, and Stefano Soatto. 2024 · 2024
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Adapting language models to compress contexts
Alexis Chevalier, Alexander Wettig, Anirudh Ajith, and Danqi Chen. 2023 · 2023
Cited alongside, same era.
Regavae: A retrieval-augmented gaussian mixture variational auto-encoder for language modeling
Jingcheng Deng, Liang Pang, Huawei Shen, and Xueqi Cheng. 2023 · 2023
Cited alongside, same era.
Towards general text embeddings with multi-stage contrastive learning
Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang. 2023 · 2023
Cited alongside, same era.
Learning to compress prompts with gist tokens
Jesse Mu, Xiang Li, and Noah D. Goodman. 2023 · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Llm2vec: Large language models are secretly powerful text encoders
Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau, Nicolas Chapados, and Siva Reddy. 2024 · 2024
Cited alongside, same era.
Fine-tuning llama for multi-stage text retrieval
Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, and Jimmy Lin. 2024 · 2024
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Sfr-embedding-2: Advanced text embedding with multi-stage training
Rui Meng, Ye Liu, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, and Semih Yavuz. 2024 · 2024
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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. 2024 · 2024
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REPLUG: retrieval-augmented black-box language models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Richard James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2024 · 2024
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Repetition improves language model embeddings
Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, and Aditi Raghunathan. 2024 · 2024
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Geneol: Harnessing the generative power of llms for training-free sentence embeddings
Raghuveer Thirukovalluru and Bhuwan Dhingra. 2024 · 2024
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Improving text embeddings with large language models
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei. 2024a · 2024
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Unsupervised information refinement training of large language models for retrieval-augmented generation
Shicheng Xu, Liang Pang, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, and Jie Zhou. 2024 · 2024
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Large language model is not a (multilingual) compositional relation reasoner
Jinman Zhao and Xueyan Zhang. 2024 · 2024
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Shengyao Zhuang, Xueguang Ma, Bevan Koopman, Jimmy Lin, and Guido Zuccon. 2024 · 2024
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Role-play paradox in large language models: Reasoning performance gains and ethical dilemmas
Jinman Zhao, Zifan Qian, Linbo Cao, Yining Wang, Yitian Ding, Yulan Hu, Zeyu Zhang, and Zeyong Jin. 2025 · 2025
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MTEB: massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers. 2023 · 2029
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