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Generative Large Language Models (LLMs) are widely utilized for their excellence in various tasks.
Wat zei je? detecting out-of-distribution translations with variational transformers
Tim Z. Xiao, Aidan N. Gomez, and Yarin Gal. 2020 · 2006
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E. Raftery. 2007 · 2007
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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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel 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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Contextual string embeddings for sequence labeling
Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018 · 2018
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Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 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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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 2020
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Unsupervised quality estimation for neural machine translation
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia. 2020 · 2020
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Explainable automated fact-checking for public health claims
Neema Kotonya and Francesca Toni. 2020 · 2020
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Question answering with long multiple-span answers
Ming Zhu, Aman Ahuja, Da-Cheng Juan, Wei Wei, and Chandan K. Reddy. 2020 · 2020
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Benchmarking Bayesian deep learning on diabetic retinopathy detection tasks
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W Dusenberry, Ghassen Jerfel, Dustin Tran, and Yarin Gal. 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
Cited alongside, same era.
What disease does this patient have? a large-scale open domain question answering dataset from medical exams
Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021 · 2021
Cited alongside, same era.
Uncertainty estimation in autoregressive structured prediction
Andrey Malinin and Mark Gales. 2021 · 2021
Cited alongside, same era.
Real-time uncertainty estimation in computer vision via uncertainty-aware distribution distillation
Yichen Shen, Zhilu Zhang, Mert R Sabuncu, and Lin Sun. 2021 · 2021
Cited alongside, same era.
Quantifying uncertainty in answers from any language model and enhancing their trustworthiness
Jiuhai Chen and Jonas Mueller. 2023 · 2023
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Adapting large language models via reading comprehension
Daixuan Cheng, Shaohan Huang, and Furu Wei. 2023 · 2023
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LM vs LM: Detecting factual errors via cross examination
Roi Cohen, May Hamri, Mor Geva, and Amir Globerson. 2023 · 2023
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Shifting attention to relevance: Towards the uncertainty estimation of large language models
Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, and Kaidi Xu. 2023 · 2023
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Uncertainty in natural language processing: Sources, quantification, and applications
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Tomayto, tomahto. beyond token-level answer equivalence for question answering evaluation
Jannis Bulian, Christian Buck, Wojciech Gajewski, Benjamin Börschinger, and Tal Schuster. 2022 · 2022
Cited alongside, same era.
WebQA: Multihop and Multimodal QA
Yingshan Chang, Guihong Cao, Mridu Narang, Jianfeng Gao, Hisami Suzuki, and Yonatan Bisk. 2022 · 2022
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, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, and Jared Kaplan. 2022 · 2022
Cited alongside, same era.
MedMCQA: A large-scale multi-subject multi-choice dataset for medical domain question answering
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022 · 2022
Cited alongside, same era.
Uncertainty estimation of transformer predictions for misclassification detection
Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, and Leonid Zhukov. 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.
A hierarchical attention retrieval model for healthcare question answering
Ming Zhu, Aman Ahuja, Wei Wei, and Chandan K. Reddy. 2019 · 2022
Cited alongside, same era.
Mengting Hu, Zhen Zhang, Shiwan Zhao, Minlie Huang, and Bingzhe Wu. 2023 · 2023
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Benchmarking large language models as AI research agents
Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec. 2023 · 2023
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation
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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OpenAI. 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, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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A comprehensive capability analysis of GPT-3 and GPT-3.5 series models
Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang Shen, Jie Zhou, Siming Chen, Tao Gui, Qi Zhang, and Xuanjing Huang. 2023 · 2023
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