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Recent research investigates factual knowledge stored in large pretrained language models (PLMs).
REALM: retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2002
Earlier work this paper cites.
K-adapter: Infusing knowledge into pre-trained models with adapters
Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, and Ming Zhou. 2020 · 2002
Earlier work this paper cites.
Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave. 2020 · 2007
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Benjamin Heinzerling and Kentaro Inui. 2020 · 2008
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JAKET: joint pre-training of knowledge graph and language understanding
Donghan Yu, Chenguang Zhu, Yiming Yang, and Michael Zeng. 2020 · 2010
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When do you need billions of words of pretraining data?
Yian Zhang, Alex Warstadt, Haau-Sing Li, and Samuel R. Bowman. 2020 · 2011
Earlier work this paper cites.
Japanese and korean voice search
Mike Schuster and Kaisuke Nakajima. 2012 · 2012
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomás Mikolov. 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.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
Earlier work this paper cites.
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Earlier work this paper cites.
Corpus-level fine-grained entity typing
Yadollah Yaghoobzadeh, Heike Adel, and Hinrich Schütze. 2018 · 2018
Cited alongside, same era.
COMET: commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 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.
Knowledge enhanced contextual word representations
Matthew E. Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith. 2019 · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick S. H. Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander H. Miller. 2019 · 2019
Cited alongside, same era.
Energy and policy considerations for deep learning in NLP
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.
Bert-knn: Adding a knn search component to pretrained language models for better QA
Nora Kassner and Hinrich Schütze. 2020 · 2020
Later among the works it cites.
Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
Later among the works it cites.
Pre-training via paraphrasing
Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, and Luke Zettlemoyer. 2020 · 2020
Later among the works it cites.
K-BERT: enabling language representation with knowledge graph
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020 · 2020
Later among the works it cites.
How context affects language models’ factual predictions
Fabio Petroni, Patrick S. H. Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2020 · 2020
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Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
Cited alongside, same era.
ERNIE: enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
Cited alongside, same era.
Inducing relational knowledge from BERT
Zied Bouraoui, José Camacho-Collados, and Steven Schockaert. 2020 · 2020
Cited alongside, same era.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models
Allyson Ettinger. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
E-BERT: Efficient-yet-effective entity embeddings for BERT
Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2020 · 2020
Later among the works it cites.
How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020
Later among the works it cites.
Pretrained encyclopedia: Weakly supervised knowledge-pretrained language model
Wenhan Xiong, Jingfei Du, William Yang Wang, and Veselin Stoyanov. 2020 · 2020
Later among the works it cites.
Multilingual LAMA: investigating knowledge in multilingual pretrained language models
Nora Kassner, Philipp Dufter, and Hinrich Schütze. 2021 · 2021
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