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Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario.
Roberta: A robustly optimized BERT pretraining approach
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Earlier work this paper cites.
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Earlier work this paper cites.
Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
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Earlier work this paper cites.
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Earlier work this paper cites.
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Language models are few-shot learners
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Earlier work this paper cites.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Earlier work this paper cites.
A rigorous study on named entity recognition: Can fine-tuning pretrained model lead to the promised land?
Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, and Nicholas Jing Yuan. 2020 · 2020
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. 2020 · 2020
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Simple and effective few-shot named entity recognition with structured nearest neighbor learning
Yi Yang and Arzoo Katiyar. 2020 · 2020
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition
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Measure and improve robustness in nlp models: A survey
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Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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On the transferability of pre-trained language models: A study from artificial datasets
Cheng-Han Chiang and Hung-yi Lee. 2022 · 2022
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy Liang, and Gregory Valiant. 2022 · 2022
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Few-shot named entity recognition: An empirical baseline study
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Cutting down on prompts and parameters: Simple few-shot learning with language models
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021 · 2021
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Template-free prompt tuning for few-shot NER
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Cross-task generalization via natural language crowdsourcing instructions
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021b · 2021
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Avoiding inference heuristics in few-shot prompt-based finetuning
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Ground-truth labels matter: A deeper look into input-label demonstrations
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Good examples make a faster learner: Simple demonstration-based learning for low-resource NER
Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, and Xiang Ren. 2022 · 2022
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Contrastive demonstration tuning for pre-trained language models
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Chain of thought prompting elicits reasoning in large language models
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An explanation of in-context learning as implicit bayesian inference
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