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Transformer-based language models have been shown to be highly effective for several NLP tasks.
Roberta: A robustly optimized bert pretraining approach
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Questions and answers: Semantics and logic
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Adv-bert: BERT is not robust on misspellings! generating nature adversarial samples on BERT
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A finite-state approach to events in natural language semantics
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Towards a montagovian account of dynamics
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Sdrt and continuation semantics
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Lexical meaning in context: A web of words
Asher, Nicholas. 2011 · 2011
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Message exchange games in strategic contexts
Asher, Nicholas, Soumya Paul, and Antoine Venant. 2017 · 2017
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Counterfactual fairness
Kusner, Matt J, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
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Synthetic and natural noise both break neural machine translation
Belinkov, Yonatan and Yonatan Bisk. 2018 · 2018
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Stress test evaluation for natural language inference
Naik, Aakanksha, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Yang, Zhilin, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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Fairness and Machine Learning
Barocas, Solon, Moritz Hardt, and Arvind Narayanan. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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A subregular bound on the complexity of lexical quantifiers
Graf, Thomas. 2019 · 2019
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Designing and interpreting probes with control tasks
Hewitt, John and Percy Liang. 2019 · 2019
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A structural probe for finding syntax in word representations
Hewitt, John and Christopher D. Manning. 2019 · 2019
A primer in BERTology: What we know about how BERT works
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F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering
Schuff, Hendrik, Heike Adel, and Ngoc Thang Vu. 2020 · 2020
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oLMpics-on what language model pre-training captures
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Transformers: State-of-the-art natural language processing
Wolf, Thomas, 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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Adversarial attacks on deep-learning models in natural language processing: A survey
Zhang, Wei Emma, Quan Z Sheng, Ahoud Alhazmi, and Chenliang Li. 2020 · 2020
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A multi-type multi-span network for reading comprehension that requires discrete reasoning
Hu, Minghao, Yuxing Peng, Zhen Huang, and Dongsheng Li. 2019 · 2019
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Linguistic knowledge and transferability of contextual representations
Liu, Nelson F., Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019a · 2019
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CoQA: A Conversational Question Answering Challenge
Reddy, Siva, Danqi Chen, and Christopher D. Manning. 2019 · 2019
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BERT rediscovers the classical NLP pipeline
Tenney, Ian, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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What do you learn from context? probing for sentence structure in contextualized word representations
Tenney, Ian, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Sam Bowman, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Zhilin, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Fair and adequate explanations
Asher, Nicholas, Soumya Paul, and Chris Russell. 2021 · 2021
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Evaluating large language models trained on code
Chen, Mark, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
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What’s in your head? Emergent behaviour in multi-task transformer models
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Understanding by understanding not: Modeling negation in language models
Hosseini, Arian, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, and Aaron Courville. 2021 · 2021
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Constrained language models yield few-shot semantic parsers
Shin, Richard, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, and Benjamin Van Durme. 2021 · 2021
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Universal adversarial attacks with natural triggers for text classification
Song, Liwei, Xinwei Yu, Hsuan-Tung Peng, and Karthik Narasimhan. 2021 · 2021
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Are larger pretrained language models uniformly better? comparing performance at the instance level
Zhong, Ruiqi, Dhruba Ghosh, Dan Klein, and Jacob Steinhardt. 2021 · 2021
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GPT-NeoX-20B: An open-source autoregressive language model
Black, Sidney, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, Usvsn Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. 2022 · 2022
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Are chatgpt and alphacode going to replace programmers?
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Strings from neurons to language
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Holistic evaluation of language models
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Training language models to follow instructions with human feedback
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Automatic generation of programming exercises and code explanations using large language models
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Few-shot semantic parsing with language models trained on code
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
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Adversarial examples for evaluating reading comprehension systems
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