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Pre-trained language models (PLMs) have gained increasing popularity due to their compelling prediction performance in diverse natural language processing (NLP) tasks.
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
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Verification of forecasts expressed in terms of probability
Glenn W Brier et al. 1950 · 1950
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Rational decisions
I.J. Good. 1952 · 1952
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Ensemble learning via negative correlation
Yong Liu and Xin Yao. 1999 · 1999
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Using bayesian model averaging to calibrate forecast ensembles
Adrian E Raftery, Tilmann Gneiting, Fadoua Balabdaoui, and Michael Polakowski. 2005 · 2005
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Nurses’ risk assessment judgements: A confidence calibration study
Huiqin Yang and Carl Thompson. 2010 · 2010
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Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
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Calibrating predictive model estimates to support personalized medicine
Xiaoqian Jiang, Melanie Osl, Jihoon Kim, and Lucila Ohno-Machado. 2012 · 2012
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015 · 2015
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A large annotated corpus for learning natural language inference
Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. 2017 · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017 · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal. 2017 · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez, and Steffen Udluft. 2018 · 2018
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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon. 2018 · 2018
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Trainable calibration measures for neural networks from kernel mean embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain. 2018 · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
Cited alongside, same era.
Analyzing uncertainty in neural machine translation
Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. 2018 · 2018
Cited alongside, same era.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 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
Uncertainty-aware semantic augmentation for neural machine translation
Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Luxi Xing, and Weihua Luo. 2020 · 2020
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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, et al. 2020 · 2020
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Uncertainty-aware curriculum learning for neural machine translation
Yikai Zhou, Baosong Yang, Derek F Wong, Yu Wan, and Lidia S Chao. 2020 · 2020
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, et al. 2021 · 2021
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Getting a CLUE: A method for explaining uncertainty estimates
Javier Antoran, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2021 · 2021
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Cited alongside, same era.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton. 2019 · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. 2019 · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
Cited alongside, same era.
Pre-trained language model for biomedical question answering
Wonjin Yoon, Jinhyuk Lee, Donghyeon Kim, Minbyul Jeong, and Jaewoo Kang. 2019 · 2019
Cited alongside, same era.
Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
ELECTRA: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
Cited alongside, same era.
A comparison of pre-trained language models for multi-class text classification in the financial domain
Yusuf Arslan, Kevin Allix, Lisa Veiber, Cedric Lothritz, Tegawendé F Bissyandé, Jacques Klein, and Anne Goujon. 2021 · 2021
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Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Umang Bhatt, Javier Antorán, Yunfeng Zhang, Q Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, et al. 2021 · 2021
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Beyond pinball loss: Quantile methods for calibrated uncertainty quantification
Youngseog Chung, Willie Neiswanger, Ian Char, and Jeff Schneider. 2021 · 2021
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On the effects of transformer size on in-and out-of-domain calibration
Soham Dan and Dan Roth. 2021 · 2021
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A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon. 2021 · 2021
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Effectively leveraging bert for legal document classification
Nut Limsopatham. 2021 · 2021
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Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, and Dan Roth. 2021 · 2021
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Revisiting the calibration of modern neural networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic. 2021 · 2021
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Improving calibration through the relationship with adversarial robustness
Yao Qin, Xuezhi Wang, Alex Beutel, and Ed Chi. 2021 · 2021
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On calibration and out-of-domain generalization
Yoav Wald, Amir Feder, Daniel Greenfeld, and Uri Shalit. 2021 · 2021
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The art of abstention: Selective prediction and error regularization for natural language processing
Ji Xin, Raphael Tang, Yaoliang Yu, and Jimmy Lin. 2021 · 2021
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Smedbert: A knowledge-enhanced pre-trained language model with structured semantics for medical text mining
Taolin Zhang, Zerui Cai, Chengyu Wang, Minghui Qiu, Bite Yang, and Xiaofeng He. 2021 · 2021
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Document-level relation extraction with adaptive thresholding and localized context pooling
Wenxuan Zhou, Kevin Huang, Tengyu Ma, and Jing Huang. 2021 · 2021
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Bayesian-torch: Bayesian neural network layers for uncertainty estimation
Ranganath Krishnan, Pi Esposito, and Mahesh Subedar. 2022 · 2022
Closest in time.
Diverse, global and amortised counterfactual explanations for uncertainty estimates
Dan Ley, Umang Bhatt, and Adrian Weller. 2022 · 2022
Closest in time.
Sais: Supervising and augmenting intermediate steps for document-level relation extraction
Yuxin Xiao, Zecheng Zhang, Yuning Mao, Carl Yang, and Jiawei Han. 2022 · 2022
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