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The impressive performance of GPT-3 using natural language prompts and in-context learning has inspired work on better fine-tuning of moderately-sized models under this paradigm.
mixup: Beyond empirical risk minimization
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Task meta-transfer from limited parallel labels
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Supervised contrastive learning
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Few-shot learning through contextual data augmentation
Farid Arthaud, Rachel Bawden, and Alexandra Birch. 2021 · 2021
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Semi-supervised few-shot intent classification and slot filling
Samyadeep Basu, Karine lp Kiun Chong, Amr Sharaf, Alex Fischer, Vishal Rohra, Michael Amoake, Hazem El-Hammamy, Ehi Nosakhare, Vijay Ramani, and Benjamin Han. 2021 · 2021
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Making pre-trained language models better few-shot learners
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Metapix: Domain transfer for semantic segmentation by meta pixel weighting
Towards understanding and mitigating social biases in language models
Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2021 · 2021
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Bootstrapping semantic segmentation with regional contrast
Shikun Liu, Shuaifeng Zhi, Edward Johns, and Andrew J Davison. 2021 · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021 · 2021
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Improving and simplifying pattern exploiting training
Derek Tam, Rakesh R Menon, Mohit Bansal, Shashank Srivastava, and Colin Raffel. 2021 · 2021
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Few-shot text classification with triplet networks, data augmentation, and curriculum learning
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Yiren Jian and Chongyang Gao. 2021 · 2021
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AEDA: an easier data augmentation technique for text classification
Akbar Karimi, Leonardo Rossi, and Andrea Prati. 2021 · 2021
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Semi-supervised meta-learning for cross-domain few-shot intent classification
Judith Yue Li and Jiong Zhang. 2021 · 2021
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020a
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. 2020b
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Contrastive representation distillation
Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020b
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What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. 2020c
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Calibrate Before Use: Improving Few-shot Performance of Language Models
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Embedding hallucination for few-shot language learning
Yiren Jian, Chongyang Gao, and Soroush Vosoughi. 2022 · 2022
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Label hallucination for few-shot classification
Yiren Jian and Lorenzo Torresani. 2022 · 2022
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