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The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining.
Why do masked neural language models still need common sense knowledge?
Sunjae Kwon, Cheongwoong Kang, Jiyeon Han, and Jaesik Choi. 2019 · 1911
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Exploiting cloze questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2020 · 2001
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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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Unsupervised commonsense question answering with self-talk
Vered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2004
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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, et al. 2020 · 2005
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The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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HuggingFace’s Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi 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 M. Rush. 2019 · 2010
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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A SICK cure for the evaluation of compositional distributional semantic models
Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, Roberto Zamparelli, et al. 2014 · 2014
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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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First quora dataset release: Question pairs
Shankar Iyer, Nikhil Dandekar, and Kornel Csernai. 2017 · 2017
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Context-aware representations for knowledge base relation extraction
Daniil Sorokin and Iryna Gurevych. 2017 · 2017
Cited alongside, same era.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, Germán Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
T-REx: A large scale alignment of natural language with knowledge base triples
Hady ElSahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon S. Hare, Frédérique Laforest, and Elena Simperl. 2018 · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Unsupervised question answering by cloze translation
Patrick Lewis, Ludovic Denoyer, and Sebastian Riedel. 2019 · 2019
Later among the works it cites.
Linguistic knowledge and transferability of contextual representations
Nelson F Liu, Matt Gardner, Yonatan Belinkov, Matthew Peters, and Noah A Smith. 2019 · 2019
Later among the works it cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
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Trieu H Trinh and Quoc V Le. 2018 · 2018
Cited alongside, same era.
Inducing relational knowledge from BERT
Zied Bouraoui, Jose Camacho-Collados, and Steven Schockaert. 2019 · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Designing and interpreting probes with control tasks
John Hewitt and Percy Liang. 2019 · 2019
Cited alongside, same era.
Attention is not explanation
Sarthak Jain and Byron C Wallace. 2019 · 2019
Cited alongside, same era.
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2019 · 2019
Later among the works it cites.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
Later among the works it cites.
How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Closest in time.
On the stability of fine-tuning bert: Misconceptions, explanations, and strong baselines
Marius Mosbach, Maksym Andriushchenko, and Dietrich Klakow. 2020 · 2020
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Information-theoretic probing with minimum description length
Elena Voita and Ivan Titov. 2020 · 2020
Closest in time.