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Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks.
Language models are few-shot learners
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Representation learning: A review and new perspectives
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Efficient estimation of word representations in vector space
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Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
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Skip-thought vectors
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Enriching word vectors with subword information
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Attention is all you need
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An improved text sentiment classification model using TF-IDF and next word negation
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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code2vec: Learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019 · 2019
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
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Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019a · 2019
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How multilingual is multilingual BERT?
Telmo Pires, Eva Schlinger, and Dan Garrette. 2019 · 2019
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A simple framework for contrastive learning of visual representations. In International conference on machine learning . PMLR, 1597–1607
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
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Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk. 2020 · 2020
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mT5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2020 · 2020
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A short survey of pre-trained language models for conversational ai-a new age in nlp. In Proceedings of the Australasian computer science week multiconference . 1–4
Munazza Zaib, Quan Z Sheng, and Wei Emma Zhang. 2020 · 2020
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Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text
Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong. 2021 · 2021
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. 2021a · 2021
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Vlmo: Unified vision-language pre-training with mixture-of-modality-experts
Hangbo Bao, Wenhui Wang, Li Dong, Qiang Liu, Owais Khan Mohammed, Kriti Aggarwal, Subhojit Som, and Furu Wei. 2021b · 2021
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Condenser: a Pre-training Architecture for Dense Retrieval. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 981–993
Luyu Gao and Jamie Callan. 2021a · 2021
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Unsupervised corpus aware language model pre-training for dense passage retrieval
Luyu Gao and Jamie Callan. 2021b · 2021
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SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 6894–6910
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. 2021 · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2021 · 2021
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Multi-task retrieval for knowledge-intensive tasks
Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oğuz, Veselin Stoyanov, and Gargi Ghosh. 2021 · 2021
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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, et al · 2021
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Learning transferable visual models from natural language supervision. In International conference on machine learning . PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al · 2021
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RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 2825–2835
Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, Qiaoqiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2021 · 2021
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2021
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Defsent: Sentence embeddings using definition sentences
Hayato Tsukagoshi, Ryohei Sasano, and Koichi Takeda. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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Fundamentals of deep learning
Nithin Buduma, Nikhil Buduma, and Joe Papa. 2022 · 2022
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HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization. In The Eleventh International Conference on Learning Representations
ZeFeng Cai, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Xin Alex Lin, Liang He, and Daxin Jiang. 2022 · 2022
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Reciprocal learning of knowledge retriever and response ranker for knowledge-grounded conversations. In Proceedings of the 29th International Conference on Computational Linguistics . 389–399
Jiazhan Feng, Chongyang Tao, Zhen Li, Chang Liu, Tao Shen, and Dongyan Zhao. 2022 · 2022
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Unixcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
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Promptbert: Improving bert sentence embeddings with prompts
Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, and Qi Zhang. 2022 · 2022
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Matryoshka representation learning
Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham Kakade, Prateek Jain, et al · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning . PMLR, 12888–12900
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022 · 2022
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SGPT: GPT Sentence Embeddings for Semantic Search
Niklas Muennighoff. 2022 · 2022
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MTEB: Massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers. 2022 · 2022
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Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. In Findings of the Association for Computational Linguistics: ACL 2022 , Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds.). Association for Computational Linguistics, Dublin, Ireland, 1864–1874
Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith Hall, Daniel Cer, and Yinfei Yang. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Parallel context windows for large language models
Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Inbal Magar, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2022 · 2022
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LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval. In The Eleventh International Conference on Learning Representations
Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Xiaolong Huang, Binxing Jiao, Linjun Yang, and Daxin Jiang. 2022 · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
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Simlm: Pre-training with representation bottleneck for dense passage retrieval
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022a · 2022
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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. 2022b · 2022
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Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022 · 2022
Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model
BigScience Workshop, Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, et al · 2022
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RetroMAE: Pre-training retrieval-oriented language models via masked auto-encoder
Shitao Xiao, Zheng Liu, Yingxia Shao, and Zhao Cao. 2022 · 2022
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Coca: Contrastive captioners are image-text foundation models
Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. 2022 · 2022
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AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels
Lei Li, Xiangxu Zhang, Xiao Zhou, and Zheng Liu. 2024f · 2024
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Mingxin Li, Zhijie Nie, Yanzhao Zhang, Dingkun Long, Richong Zhang, and Pengjun Xie. 2024b · 2024
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JMTEB: Japanese Massive Text Embedding Benchmark
Shengzhe Li, Masaya Ohagi, and Ryokan Ri. 2024c · 2024
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Conan-Embedding: General Text Embedding with More and Better Negative Samples
Shiyu Li, Yang Tang, Shizhe Chen, and Xi Chen. 2024e · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024b · 2024
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Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, et al · 2022
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Towards robust ranker for text retrieval
Yucheng Zhou, Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Binxing Jiao, and Daxin Jiang. 2022 · 2022
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GPT-4 Technical Report
OpenAI Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haim ing Bao, Mo Bavarian, Jeff Belgum, Irwan Bello, Jake Berdine, Gabriel Bernadett-Shapiro, Christopher Berner, Lenny Bogdonoff, Oleg Boiko, Made laine Boyd, Anna-Luisa Brakman, Greg Brockman, Tim Brooks, Miles Brundage, Kevin Button, Trevor Cai, Rosie Campbell, Andrew Cann, Brittany Carey, Chelsea Carlson, Rory Carmichael, Brooke Chan, Che Chang, Fotis Chantzis, Derek Chen, Sully Chen, Ruby Chen, Jason Chen, Mark Chen, Benjamin Chess, Chester Cho, Casey Chu, Hyung Won Chung, Dave Cummings, Jeremiah Currier, Yunxing Dai, Cory Decareaux, Thomas Degry, Noah Deutsch, Damien Deville, Arka Dhar, David Dohan, Steve Dowling, Sheila Dunning, Adrien Ecoffet, Atty Eleti, Tyna Eloundou, David Farhi, Liam Fedus, Niko Felix, Sim’on Posada Fishman, Juston Forte, Is abella Fulford, Leo Gao, Elie Georges, Christian Gibson, Vik Goel, Tarun Gogineni, Gabriel Goh, Raphael Gontijo-Lopes, Jonathan Gordon, Morgan Grafstein, Scott Gray, Ryan Greene, Joshua Gross, Shixiang Shane Gu, Yufei Guo, Chris Hallacy, Jesse Han, Jeff Harris, Yuchen He, Mike Heaton, Jo hannes Heidecke, Chris Hesse, Alan Hickey, Wade Hickey, Peter Hoeschele, Brandon Houghton, Kenny Hsu, Shengli Hu, Xin Hu, Joost Huizinga, Shantanu Jain, Shawn Jain, Joanne Jang, Angela Jiang, Roger Jiang, Haozhun Jin, Denny Jin, Shino Jomoto, Billie Jonn, Heewoo Jun, Tomer Kaftan, Lukasz Kaiser, Ali Kamali, Ingmar Kanitscheider, Nitish Shirish Keskar, Tabarak Khan, Logan Kilpatrick, Jong Wook Kim, Christina Kim, Yongjik Kim, Hendrik Kirchner, Jamie Ryan Kiros, Matthew Knight, Daniel Kokotajlo, Lukasz Kondraciuk, Andrew Kondrich, Aris Konstantinidis, Kyle Kosic, Gretchen Krueger, Vishal Kuo, Michael Lampe, Ikai Lan, Teddy Lee, Jan Leike, Jade Leung, Daniel Levy, Chak Ming Li, Rachel Lim, Molly Lin, Stephanie Lin, Ma teusz Litwin, Theresa Lopez, Ryan Lowe, Patricia Lue, Anna Makanju, Kim Malfacini, Sam Manning, Todor Markov, Yaniv Markovski, Bianca Martin, Katie Mayer, Andrew Mayne, Bob McGrew, Scott Mayer McKinney, Christine McLeavey, Paul McMillan, Jake McNeil, David Medina, Aalok Mehta, Jacob Menick, Luke Metz, Andrey Mishchenko, Pamela Mishkin, Vinnie Monaco, Evan Morikawa, Daniel P. Mossing, Tong Mu, Mira Murati, Oleg Murk, David M’ely, Ashvin Nair, Reiichiro Nakano, Rajeev Nayak, Arvind Neelakantan, Richard Ngo, Hyeonwoo Noh, Ouyang Long, Cullen O’Keefe, Jakub W. Pachocki, Alex Paino, Joe Palermo, Ashley Pantuliano, Giambattista Parascandolo, Joel Parish, Emy Parparita, Alexandre Passos, Mikhail Pavlov, Andrew Peng, Adam Perelman, Filipe de Avila Belbute Peres, Michael Petrov, Henrique Pondé de Oliveira Pinto, Michael Pokorny, Michelle Pokrass, Vitchyr H. Pong, Tolly Powell, Alethea Power, Boris Power, Elizabeth Proehl, Raul Puri, Alec Radford, Jack W. Rae, Aditya Ramesh, Cameron Raymond, Francis Real, Kendra Rimbach, Carl Ross, Bob Rotsted, Henri Roussez, Nick Ryder, Mario D. Saltarelli, Ted Sanders, Shibani Santurkar, Girish Sastry, Heather Schmidt, David Schnurr, John Schulman, Daniel Selsam, Kyla Sheppard, Toki Sherbakov, Jessica Shieh, Sarah Shoker, Pranav Shyam, Szymon Sidor, Eric Sigler, Maddie Simens, Jordan Sitkin, Katarina Slama, Ian Sohl, Benjamin D. Sokolowsky, Yang Song, Natalie Staudacher, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas A. Tezak, Madeleine Thompson, Phil Tillet, Amin Tootoonchian, Elizabeth Tseng, Preston Tuggle, Nick Turley, Jerry Tworek, Juan Felipe Cer’on Uribe, Andrea Vallone, Arun Vijayvergiya, Chelsea Voss, Carroll L. Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, Jason Wei, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Qim ing Yuan, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, and Barret Zoph. 2023 · 2023
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Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
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Longlora: Efficient fine-tuning of long-context large language models
Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, and Jiaya Jia. 2023 · 2023
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2023
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Qwen-audio: Advancing universal audio understanding via unified large-scale audio-language models
Yunfei Chu, Jin Xu, Xiaohuan Zhou, Qian Yang, Shiliang Zhang, Zhijie Yan, Chang Zhou, and Jingren Zhou. 2023 · 2023
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Ranking of large language model (llm) regional bias
Yucong Duan, Fuliang Tang, Kunguang Wu, Zhendong Guo, Shuaishuai Huang, Yingtian Mei, Yuxing Wang, Zeyu Yang, and Shiming Gong. 2023 · 2023
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Codexembed: A generalist embedding model family for multiligual and multi-task code retrieval
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Fine-tuning llama for multi-stage text retrieval. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2421–2425
Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, and Jimmy Lin. 2024 · 2024
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NV-Retriever: Improving text embedding models with effective hard-negative mining
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Generative representational instruction tuning
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A Text is Worth Several Tokens: Text Embedding from LLMs Secretly Aligns Well with The Key Tokens
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Nomic embed: Training a reproducible long context text embedder
Zach Nussbaum, John X Morris, Brandon Duderstadt, and Andriy Mulyar. 2024 · 2024
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INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models
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Improving Multi-lingual Alignment Through Soft Contrastive Learning
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Text clustering with LLM embeddings
Alina Petukhova, Joao P Matos-Carvalho, and Nuno Fachada. 2024 · 2024
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MosaicBERT: a bidirectional encoder optimized for fast pretraining
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PL-MTEB: Polish Massive Text Embedding Benchmark
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Challenging Fairness: A Comprehensive Exploration of Bias in LLM-Based Recommendations
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Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages
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Extensible Embedding: A Flexible Multipler For LLM’s Context Length
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Clinical information retrieval: A literature review
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Jina-Embeddings-v3: Multilingual Embeddings With Task LoRA
Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, Michael Günther, Bo Wang, Markus Krimmel, Feng Wang, Georgios Mastrapas, Andreas Koukounas, Nan Wang, and Han Xiao. 2024 · 2024
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. 2024 · 2024
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MAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Weiwei Sun, Zhengliang Shi, Jiulong Wu, Lingyong Yan, Xinyu Ma, Yiding Liu, Min Cao, Dawei Yin, and Zhaochun Ren. 2024 · 2024
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ADAM: Dense Retrieval Distillation with Adaptive Dark Examples. In Findings of the Association for Computational Linguistics ACL 2024 . 11639–11651
Chongyang Tao, Chang Liu, Tao Shen, Can Xu, Xiubo Geng, Binxing Jiao, and Daxin Jiang. 2024 · 2024
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GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings
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Preserving Multilingual Quality While Tuning Query Encoder on English Only
Oleg Vasilyev, Randy Sawaya, and John Bohannon. 2024 · 2024
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voyage-large-2-instruct: Instruction-tuned and rank 1 on MTEB
Voyage-AI. 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. 2024c · 2024
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Resonance rope: Improving context length generalization of large language models
Suyuchen Wang, Ivan Kobyzev, Peng Lu, Mehdi Rezagholizadeh, and Bang Liu. 2024a · 2024
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BIRCO: A Benchmark of Information Retrieval Tasks with Complex Objectives
Xiaoyue Wang, Jianyou Wang, Weili Cao, Kaicheng Wang, Ramamohan Paturi, and Leon Bergen. 2024b · 2024
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German text embedding clustering benchmark
Silvan Wehrli, Bert Arnrich, and Christopher Irrgang. 2024 · 2024
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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 · 2024
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Rar-b: Reasoning as retrieval benchmark
Chenghao Xiao, G Thomas Hudson, and Noura Al Moubayed. 2024a · 2024
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A survey on knowledge distillation of large language models
Xiaohan Xu, Ming Li, Chongyang Tao, Tao Shen, Reynold Cheng, Jinyang Li, Can Xu, Dacheng Tao, and Tianyi Zhou. 2024 · 2024
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Langbridge: Multilingual reasoning without multilingual supervision
Dongkeun Yoon, Joel Jang, Sungdong Kim, Seungone Kim, Sheikh Shafayat, and Minjoon Seo. 2024 · 2024
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Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models
Bowen Zhang, Kehua Chang, and Chunping Li. 2024a · 2024
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Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, et al · 2024
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Recommender systems in the era of large language models (llms)
Zihuai Zhao, Wenqi Fan, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, et al · 2024
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VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval
Junjie Zhou, Zheng Liu, Shitao Xiao, Bo Zhao, and Yongping Xiong. 2024 · 2024
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LongEmbed: Extending Embedding Models for Long Context Retrieval
Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, and Sujian Li. 2024 · 2024
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PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida, USA, 4375–4391
Shengyao Zhuang, Xueguang Ma, Bevan Koopman, Jimmy Lin, and Guido Zuccon. 2024 · 2024
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Multi-Lingual Malaysian Embedding: Leveraging Large Language Models for Semantic Representations
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Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval
Kidist Amde Mekonnen, Yosef Worku Alemneh, and Maarten de Rijke. 2025 · 2025
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TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection
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Semi-Supervised Anomaly Detection through Denoising-Aware Contrastive Distance Learning. In Proceedings of the ACM on Web Conference 2025 . 2111–2119
Jianling Gao, Chongyang Tao, Zhenchao Sun, Xiya Jiang, and Shuai Ma. 2025 · 2025
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Building Russian Benchmark for Evaluation of Information Retrieval Models
Grigory Kovalev, Mikhail Tikhomirov, Evgeny Kozhevnikov, Max Kornilov, and Natalia Loukachevitch. 2025 · 2025
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Towards A Generalist Code Embedding Model Based On Massive Data Synthesis
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Reinforced Information Retrieval
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Text clustering with large language model embeddings
Alina Petukhova, Joao P Matos-Carvalho, and Nuno Fachada. 2025 · 2025
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Tingyu Song, Guo Gan, Mingsheng Shang, and Yilun Zhao. 2025 · 2025
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BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Haisu Liu, Quan Shi, Zachary S. Siegel, Michael Tang, Ruoxi Sun, Jinsung Yoon, Sercan O. Arik, Danqi Chen, and Tao Yu. 2025 · 2025
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Hao Sun, Yunyi Shen, Jean-Francois Ton, and Mihaela van der Schaar. 2025b · 2025
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Finmteb: Finance massive text embedding benchmark
Yixuan Tang and Yi Yang. 2025 · 2025
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mFollowIR: A Multilingual Benchmark for Instruction Following in Retrieval
Orion Weller, Benjamin Chang, Eugene Yang, Mahsa Yarmohammadi, Sam Barham, Sean MacAvaney, Arman Cohan, Luca Soldaini, Benjamin Van Durme, and Dawn Lawrie. 2025 · 2025
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A Reasoning-Focused Legal Retrieval Benchmark. In Proceedings of the Symposium on Computer Science and Law on ZZZ . ACM, Munich Germany, 169–193
Lucia Zheng, Neel Guha, Javokhir Arifov, Sarah Zhang, Michal Skreta, Christopher D. Manning, Peter Henderson, and Daniel E. Ho. 2025 · 2025
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Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models
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Famteb: Massive text embedding benchmark in persian language
Erfan Zinvandi, Morteza Alikhani, Mehran Sarmadi, Zahra Pourbahman, Sepehr Arvin, Reza Kazemi, and Arash Amini. 2025 · 2025
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