Fetching the paper…
Reading the bibliography…
Petroni et al.
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. 2019 · 1907
Earlier work this paper cites.
BERT is not a knowledge base (yet): Factual knowledge vs. name-based reasoning in unsupervised qa
Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2019 · 1911
Earlier work this paper cites.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020 · 2004
Earlier work this paper cites.
StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2020 · 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.
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.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2020 · 2012
Earlier work this paper cites.
Representing general relational knowledge in conceptnet 5
Robyn Speer and Catherine Havasi. 2012 · 2012
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
What do neural machine translation models learn about morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass. 2017 · 2017
Earlier work this paper cites.
T-REx: A large scale alignment of natural language with knowledge base triples
Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, and Elena Simperl. 2018 · 2018
Earlier work this paper cites.
What makes reading comprehension questions easier?
Saku Sugawara, Kentaro Inui, Satoshi Sekine, and Akiko Aizawa. 2018 · 2018
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.
ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
Cited alongside, same era.
Open sesame: Getting inside bert’s linguistic knowledge
Yongjie Lin, Yi Chern Tan, and Robert Frank. 2019 · 2019
Cited alongside, same era.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Later among the works it cites.
Information-theoretic probing for linguistic structure
Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, Ran Zmigrod, Adina Williams, and Ryan Cotterell. 2020 · 2020
Later among the works it cites.
A primer in bertology: What we know about how bert works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2020
Later among the works it cites.
AutoPrompt: Automatic prompt construction for masked language models
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Later among the works it cites.
Information-theoretic probing with minimum description length
Elena Voita and Ivan Titov. 2020 · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Julien Chaumond, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, et al. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
Cited alongside, same era.
What do you learn from context? probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel Bowman, Dipanjan Das, et al. 2019 · 2019
Cited alongside, same era.
Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 2019
Cited alongside, same era.
Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
An information theoretic view on selecting linguistic probes
Zining Zhu and Frank Rudzicz. 2020 · 2020
Later among the works it cites.
BERTese: Learning to speak to BERT
Adi Haviv, Jonathan Berant, and Amir Globerson. 2021 · 2021
Closest in time.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Closest in time.
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021 · 2021
Closest in time.