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The remarkable performance of large language models (LLMs) in zero-shot language understanding has garnered significant attention.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio. 2004 · 2004
Earlier work this paper cites.
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, et al. 2020 · 2005
Earlier work this paper cites.
Semi-supervised classification by low density separation
Olivier Chapelle and Alexander Zien. 2005 · 2005
Earlier work this paper cites.
Entropy regularization
Yves Grandvalet and Yoshua Bengio. 2006 · 2006
Earlier work this paper cites.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2020 · 2009
Earlier work this paper cites.
Contrastive distillation on intermediate representations for language model compression
Siqi Sun, Zhe Gan, Yu Cheng, Yuwei Fang, Shuohang Wang, and Jingjing Liu. 2020 · 2009
Earlier work this paper cites.
Text classification using label names only: A language model self-training approach
Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, and Jiawei Han. 2020 · 2010
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al. 2013 · 2013
Earlier work this paper cites.
Unsupervised deep embedding for clustering analysis
Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016 · 2016
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton. 2019 · 2019
Earlier work this paper cites.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2020
Earlier work this paper cites.
A survey on semi-supervised learning
Jesper E Van Engelen and Holger H Hoos. 2020 · 2020
Cited alongside, same era.
Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp
Timo Schick, Sahana Udupa, and Hinrich Schütze. 2021 · 2021
Cited alongside, same era.
Few-shot text classification with triplet networks, data augmentation, and curriculum learning 2021
J Wei, C Huang, S Vosoughi, Y Cheng, and S Xu. 2021 · 2021
Cited alongside, same era.
Gpt3mix: Leveraging large-scale language models for text augmentation
Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, and Woomyeong Park. 2021 · 2021
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Later among the works it cites.
Data augmentation for intent classification with off-the-shelf large language models
Gaurav Sahu, Pau Rodriguez, Issam H Laradji, Parmida Atighehchian, David Vazquez, and Dzmitry Bahdanau. 2022 · 2022
Later among the works it cites.
Nearest neighbor zero-shot inference
Weijia Shi, Julian Michael, Suchin Gururangan, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
Zerogen: Efficient zero-shot learning via dataset generation
Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, and Lingpeng Kong. 2022 · 2022
Later among the works it cites.
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Cited alongside, same era.
Weakly-supervised text classification based on keyword graph
Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, and Shuigeng Zhou. 2021 · 2021
Cited alongside, same era.
Yu Fei, Ping Nie, Zhao Meng, Roger Wattenhofer, and Mrinmaya Sachan. 2022 · 2022
Cited alongside, same era.
Zerogen + + : Self-guided high-quality data generation in efficient zero-shot learning
Jiahui Gao, Renjie Pi, Yong Lin, Hang Xu, Jiacheng Ye, Zhiyong Wu, Xiaodan Liang, Zhenguo Li, and Lingpeng Kong. 2022 · 2022
Cited alongside, same era.
Zero-shot text classification with self-training
Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, and Noam Slonim. 2022 · 2022
Cited alongside, same era.
Jimin Hong, Jungsoo Park, Daeyoung Kim, Seongjae Choi, Bokyung Son, and Jaewook Kang. 2022 · 2022
Cited alongside, same era.
Unnatural instructions: Tuning language models with (almost) no human labor
Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick. 2022 · 2022
Cited alongside, same era.
Generating training data with language models: Towards zero-shot language understanding
Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han. 2022 · 2022
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023b · 2023
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Celda: Leveraging black-box language model as enhanced classifier without labels
Hyunsoo Cho, Youna Kim, and Sang-goo Lee. 2023 · 2023
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Distilling reasoning capabilities into smaller language models
Kumar Shridhar, Alessandro Stolfo, and Mrinmaya Sachan. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Pesco: Prompt-enhanced self contrastive learning for zero-shot text classification
Yau-Shian Wang, Ta-Chung Chi, Ruohong Zhang, and Yiming Yang. 2023 · 2023
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Ruohong Zhang, Yau-Shian Wang, Yiming Yang, Donghan Yu, Tom Vu, and Likun Lei. 2023 · 2023
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