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In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the prompt.
Uncertainty estimation in autoregressive structured prediction
Andrey Malinin and Mark Gales. 2020 · 2002
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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 2003
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Distinguishing and integrating aleatoric and epistemic variation in uncertainty quantification
Kamaljit Chowdhary and Paul Dupuis. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
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Good debt or bad debt: Detecting semantic orientations in economic texts
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala. 2014 · 2014
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
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Uncertainty decomposition in bayesian neural networks with latent variables
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft. 2017 · 2017
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CARER: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018 · 2018
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman. 2019 · 2019
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Quantifying uncertainties in natural language processing tasks
Yijun Xiao and William Yang Wang. 2019 · 2019
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Uncertainty aware semi-supervised learning on graph data
Xujiang Zhao, Feng Chen, Shu Hu, and Jin-Hee Cho. 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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Explicitly capturing relations between entity mentions via graph neural networks for domain-specific named entity recognition
Pei Chen, Haibo Ding, Jun Araki, and Ruihong Huang. 2021 · 2021
Cited alongside, same era.
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 · 2021
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On hallucination and predictive uncertainty in conditional language generation
Yijun Xiao and William Yang Wang. 2021 · 2021
Cited alongside, same era.
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021 · 2021
Cited alongside, same era.
Crossroads, buildings and neighborhoods: A dataset for fine-grained location recognition
Pei Chen, Haotian Xu, Cheng Zhang, and Ruihong Huang. 2022 · 2022
Knowledge of knowledge: Exploring known-unknowns uncertainty with large language models
Alfonso Amayuelas, Liangming Pan, Wenhu Chen, and William Wang. 2023 · 2023
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Lm-polygraph: Uncertainty estimation for language models
Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, et al. 2023 · 2023
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Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023 · 2023
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Generating with confidence: Uncertainty quantification for black-box large language models
Zhen Lin, Shubhendu Trivedi, and Jimeng Sun. 2023 · 2023
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Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Towards collaborative neural-symbolic graph semantic parsing via uncertainty
Zi Lin, Jeremiah Zhe Liu, and Jingbo Shang. 2022 · 2022
Cited alongside, same era.
Source localization of graph diffusion via variational autoencoders for graph inverse problems
Chen Ling, Junji Jiang, Junxiang Wang, and Zhao Liang. 2022 · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Uncertainty quantification with pre-trained language models: A large-scale empirical analysis
Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2022 · 2022
Cited alongside, same era.
Efficient uncertainty quantification for multilabel text classification
Jialin Yu, Alexandra I Cristea, Anoushka Harit, Zhongtian Sun, Olanrewaju Tahir Aduragba, Lei Shi, and Noura Al Moubayed. 2022 · 2022
Cited alongside, same era.
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, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Open-ended commonsense reasoning with unrestricted answer candidates
Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, and Liang Zhao. 2023a · 2023
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A survey of hallucination in large foundation models
Vipula Rawte, Amit Sheth, and Amitava Das. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, and Eyke Hüllermeier. 2023 · 2023
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Navigating the grey area: Expressions of overconfidence and uncertainty in language models
Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto. 2023 · 2023
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Beyond efficiency: A systematic survey of resource-efficient large language models
Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, et al. 2024 · 2024
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Hytrel: Hypergraph-enhanced tabular data representation learning
Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, and George Karypis. 2024 · 2024
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Elad: Explanation-guided large language models active distillation
Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, and Liang Zhao. 2024 · 2024
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