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This study aims to address the pervasive challenge of quantifying uncertainty in large language models (LLMs) without logit-access.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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Algorithmic learning in a random world , volume 29
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer. 2005 · 2005
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk. 2008 · 2008
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Uncertainty sets for image classifiers using conformal prediction
Anastasios Angelopoulos, Stephen Bates, Jitendra Malik, and Michael I Jordan. 2020 · 2009
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2009
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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Posterior calibration and exploratory analysis for natural language processing models
Khanh Nguyen and Brendan O’Connor. 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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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 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 · 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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Learning confidence for out-of-distribution detection in neural networks
Terrance DeVries and Graham W. Taylor. 2018 · 2018
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Least ambiguous set-valued classifiers with bounded error levels
Mauricio Sadinle, Jing Lei, and Larry Wasserman. 2019 · 2019
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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 2020
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Bert-based conformal predictor for sentiment analysis
Lysimachos Maltoudoglou, Andreas Paisios, and Harris Papadopoulos. 2020 · 2020
Cited alongside, same era.
A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates. 2021 · 2021
Cited alongside, same era.
Learn then test: Calibrating predictive algorithms to achieve risk control
Anastasios N Angelopoulos, Stephen Bates, Emmanuel J Candès, Michael I Jordan, and Lihua Lei. 2021 · 2021
Cited alongside, same era.
Conformal prediction for text infilling and part-of-speech prediction
Neil Dey, Jing Ding, Jack Ferrell, Carolina Kapper, Maxwell Lovig, Emiliano Planchon, and Jonathan P Williams. 2021 · 2021
Cited alongside, same era.
Prevent the language model from being overconfident in neural machine translation
Applying the conformal prediction paradigm for the uncertainty quantification of an end-to-end automatic speech recognition model (wav2vec 2.0)
Fares Ernez, Alexandre Arnold, Audrey Galametz, Catherine Kobus, and Nawal Ould-Amer. 2023 · 2023
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Conformal prediction for deep classifier via label ranking
Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, and Hongxin Wei. 2023 · 2023
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A review of nonconformity measures for conformal prediction in regression
Yuko Kato, David MJ Tax, and Marco Loog. 2023 · 2023
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Conformal prediction with large language models for multi-choice question answering
Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, and Andrew Beam. 2023 · 2023
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Do large language models need sensory grounding for meaning and understanding?
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Mengqi Miao, Fandong Meng, Yijin Liu, Xiao-Hua Zhou, and Jie Zhou. 2021 · 2021
Cited alongside, same era.
On the advance of making language models better reasoners
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
Cited alongside, same era.
Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022 · 2022
Cited alongside, same era.
Exploring predictive uncertainty and calibration in NLP: A study on the impact of method & data scarcity
Dennis Ulmer, Jes Frellsen, and Christian Hardmeier. 2022 · 2022
Cited alongside, same era.
Benchmarking scalable predictive uncertainty in text classification
Jordy Van Landeghem, Matthew Blaschko, Bertrand Anckaert, and Marie-Francine Moens. 2022 · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Cited alongside, same era.
Y LeCun. 2023 · 2023
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An overview of bard: an early experiment with generative ai
James Manyika and Sissie Hsiao. 2023 · 2023
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GPT-4v(ision): technical work
OpenAI. 2023 · 2023
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Evaluation of medium-large language models at zero-shot closed book generative question answering
René Peinl and Johannes Wirth. 2023 · 2023
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Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S Jaakkola, and Regina Barzilay. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, and Jennifer Wortman Vaughan. 2023 · 2023
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Equal opportunity of coverage in fair regression
Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, and Philip S Yu. 2023 · 2023
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Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al. 2023 · 2023
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Wizardlm: Empowering large language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023 · 2023
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