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Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019b · 1907
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Catastrophic forgetting in connectionist networks
R. French. 1999 · 1999
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020 · 2002
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Characterising bias in compressed models
Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, and Emily Denton. 2020 · 2010
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Improving robustness by augmenting training sentences with predicate-argument structures
Nafise Sadat Moosavi, Marcel de Boer, Prasetya Ajie Utama, and Iryna Gurevych. 2020a · 2010
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William J. Dally. 2015 · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
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Pay attention to the ending:strong neural baselines for the ROC story cloze task
Zheng Cai, Lifu Tu, and Kevin Gimpel. 2017 · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, J. Veness, G. Desjardins, Andrei A. Rusu, K. Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell. 2017 · 2017
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The effect of different writing tasks on linguistic style: A case study of the ROC story cloze task
Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, and Noah A. Smith. 2017 · 2017
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Evaluating compositionality in sentence embeddings
Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel Gershman, and Noah D. Goodman. 2018 · 2018
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Breaking NLI systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg. 2018 · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. 2018 · 2018
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How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C. Lipton. 2018 · 2018
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Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
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Performance impact caused by hidden bias of training data for recognizing textual entailment
Masatoshi Tsuchiya. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang. 2019 · 2019
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A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning. 2019 · 2019
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2020 · 2019
Cited alongside, same era.
Inoculation by fine-tuning: A method for analyzing challenge datasets
New insights and perspectives on the natural gradient method
James Martens. 2020 · 2020
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Syntactic data augmentation increases robustness to inference heuristics
Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, and Tal Linzen. 2020 · 2020
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2020
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Automatically identifying words that can serve as labels for few-shot text classification
Timo Schick, Helmut Schmid, and Hinrich Schütze. 2020 · 2020
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Towards debiasing NLU models from unknown biases
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Nelson F. Liu, Roy Schwartz, and Noah A. Smith. 2019a · 2019
Cited alongside, same era.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
Cited alongside, same era.
Analyzing compositionality-sensitivity of nli models
Yixin Nie, Yicheng Wang, and Mohit Bansal. 2019 · 2019
Cited alongside, same era.
Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Diversify your datasets: Analyzing generalization via controlled variance in adversarial datasets
Ohad Rozen, Vered Shwartz, Roee Aharoni, and Ido Dagan. 2019 · 2019
Cited alongside, same era.
Towards debiasing fact verification models
Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, and Regina Barzilay. 2019 · 2019
Cited alongside, same era.
Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020b · 2020
Later among the works it cites.
Learning which features matter: RoBERTa acquires a preference for linguistic generalizations (eventually)
Alex Warstadt, Yian Zhang, Xiaocheng Li, Haokun Liu, and Samuel R. Bowman. 2020 · 2020
Later among the works it cites.
Early exiting BERT for efficient document ranking
Ji Xin, Rodrigo Nogueira, Yaoliang Yu, and Jimmy Lin. 2020 · 2020
Later among the works it cites.
Towards interpreting and mitigating shortcut learning behavior of NLU models
Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, and Xia Hu. 2021 · 2021
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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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How many data points is a prompt worth?
Teven Le Scao and Alexander Rush. 2021 · 2021
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Predicting inductive biases of pre-trained models
Charles Lovering, Rohan Jha, Tal Linzen, and Ellie Pavlick. 2021 · 2021
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On the stability of fine-tuning BERT: misconceptions, explanations, and strong baselines
Marius Mosbach, Maksym Andriushchenko, and Dietrich Klakow. 2021 · 2021
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Learning from others’ mistakes: Avoiding dataset biases without modeling them
Victor Sanh, Thomas Wolf, Yonatan Belinkov, and Alexander M. Rush. 2021 · 2021
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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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Get your vitamin C! robust fact verification with contrastive evidence
Tal Schuster, Adam Fisch, and Regina Barzilay. 2021 · 2021
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Increasing robustness to spurious correlations using forgettable examples
Yadollah Yaghoobzadeh, Soroush Mehri, Remi Tachet des Combes, T. J. Hazen, and Alessandro Sordoni. 2021 · 2021
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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