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We introduce a method to measure uncertainty in large language models.
Paraphrase generation and information retrieval from stored text
Peter W Culicover · 1968
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Information Theory, Inference and Learning Algorithms
David Mackay · 2003
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Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics
Chin-Yew Lin and Franz Josef Och · 2004
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Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen · 2006
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A semantic similarity approach to paraphrase detection
Samuel Fernando and Mark Stevenson · 2008
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Measuring machine translation quality as semantic equivalence: A metric based on entailment features
Sebastian Padó, Daniel Cer, Michel Galley, Dan Jurafsky, and Christopher D Manning · 2009
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A survey of paraphrasing and textual entailment methods
Ion Androutsopoulos and Prodromos Malakasiotis · 2010
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Dynamic pooling and unfolding recursive autoencoders for paraphrase detection
Richard Socher, Eric Huang, Jeffrey Pennin, Christopher D Manning, and Andrew Ng · 2011
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Realformer: Transformer likes residual attention
Ruining He, Anirudh Ravula, Bhargav Kanagal, and Joshua Ainslie · 2012
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Deep learning for answer sentence selection
Lei Yu, Karl Moritz Hermann, Phil Blunsom, and Stephen Pulman · 2014
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Concrete Problems in AI Safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Uncertainty in deep learning
Yarin Gal et al · 2016
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Bilateral multi-perspective matching for natural language sentences
Zhiguo Wang, Wael Hamza, and Radu Florian · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2017
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Abstract meaning representation for paraphrase detection
Fuad Issa, Marco Damonte, Shay B Cohen, Xiaohui Yan, and Yi Chang · 2018
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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
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Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
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Sabrina J Mielke, Arthur Szlam, Y-Lan Boureau, and Emily Dinan · 2020
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Uncertainty-aware machine translation evaluation
Taisiya Glushkova, Chrysoula Zerva, Ricardo Rei, and André FT Martins · 2021
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Deup: Direct epistemic uncertainty prediction
Moksh Jain, Salem Lahlou, Hadi Nekoei, Victor Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, and Yoshua Bengio · 2021
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How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig · 2021
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Theories of Meaning
Jeff Speaks · 2021
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Kenton Murray and David Chiang · 2018
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Analyzing uncertainty in neural machine translation
Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato · 2018
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Benchmarking Bayesian Deep Learning with Diabetic Retinopathy Diagnosis
Angelos Filos, Sebastian Farquhar, Aidan N Gomez, Tim G J Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, and Yarin Gal · 2019
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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
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett · 2020
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Charformer: Fast character transformers via gradient-based subword tokenization
Yi Tay, Vinh Q Tran, Sebastian Ruder, Jai Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, and Donald Metzler · 2021
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Entailment as few-shot learner
Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, and Hao Ma · 2021
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
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Unsolved Problems in ML Safety
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield Dodds, Nova DasSarma, Eli Tran-Johnson, et al · 2022
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Towards collaborative neural-symbolic graph semantic parsing via uncertainty
Zi Lin, Jeremiah Zhe Liu, and Jingbo Shang · 2022
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A survey of evaluation metrics used for nlg systems
Ananya B Sai, Akash Kumar Mohankumar, and Mitesh M Khapra · 2022
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Uncertainty estimation and reduction of pre-trained models for text regression
Yuxia Wang, Daniel Beck, Timothy Baldwin, and Karin Verspoor · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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