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Prompt tuning is a parameter-efficient approach to adapting pre-trained language models to downstream tasks.
Roberta: A robustly optimized BERT pretraining approach
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LCQMC: A large-scale chinese question matching corpus
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DRCD: a chinese machine reading comprehension dataset
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Parameter-efficient transfer learning for NLP
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Ziran Li, Ning Ding, Zhiyuan Liu, Haitao Zheng, and Ying Shen. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019b · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Fast and slow learning of recurrent independent mechanisms
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True few-shot learning with language models
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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
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CPT: A pre-trained unbalanced transformer for both chinese language understanding and generation
Yunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, and Xipeng Qiu. 2021 · 2021
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ALBERT: A lite BERT for self-supervised learning of language representations
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CLUE: A Chinese language understanding evaluation benchmark
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Making pre-trained language models better few-shot learners
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Wudaocorpora: A super large-scale chinese corpora for pre-training language models
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Hui Zong, Jinxuan Yang, Zeyu Zhang, Zuofeng Li, and Xiaoyan Zhang. 2021 · 2021
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Black-box prompt learning for pre-trained language models
Shizhe Diao, Xuechun Li, Yong Lin, Zhichao Huang, and Tong Zhang. 2022 · 2022
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Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
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Combining modular skills in multitask learning
Edoardo Maria Ponti, Alessandro Sordoni, and Siva Reddy. 2022 · 2022
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Bbtv2: Towards a gradient-free future with large language models
Tianxiang Sun, Zhengfu He, Hong Qian, Yunhua Zhou, Xuanjing Huang, and Xipeng Qiu. 2022a · 2022
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Black-box tuning for language-model-as-a-service
Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, and Xipeng Qiu. 2022c · 2022
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Spot: Better frozen model adaptation through soft prompt transfer
Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou’, and Daniel Cer. 2022 · 2022
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
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CBLUE: A chinese biomedical language understanding evaluation benchmark
Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, and Qingcai Chen. 2022b · 2022
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