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Uncertainty estimation is a significant issue for current large language models (LLMs) that are generally poorly calibrated and over-confident, especially with reinforcement learning from human feedback (RLHF).
Verification of forecasts expressed in terms of probability
Glenn W. Brier · 1950
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 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
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Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F. Liu, and Matt Gardner · 2017
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Calibrated language model fine-tuning for in- and out-of-distribution data
Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, and Chao Zhang · 2020
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AmbigQA: Answering ambiguous open-domain questions
Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 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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Wise teamwork: Collective confidence calibration predicts the effectiveness of group discussion
Ike Silver, Barbara A. Mellers, and Philip E. Tetlock · 2021
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 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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Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Reducing conversational agents’ overconfidence through linguistic calibration
Sabrina J. Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau · 2022
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Chatgpt blog, 2022
OpenAI · 2022
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Measuring and narrowing the compositionality gap in language models
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis · 2022
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Generating with confidence: Uncertainty quantification for black-box large language models
Zhen Lin, Shubhendu Trivedi, and Jimeng Sun · 2023
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Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein · 2023
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Strength in numbers: Estimating confidence of large language models by prompt agreement
Gwenyth Portillo Wightman, Alexandra Delucia, and Mark Dredze · 2023
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Getting more out of mixture of language model reasoning experts
Chenglei Si, Weijia Shi, Chen Zhao, Luke Zettlemoyer, and Jordan Boyd-Graber · 2023
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Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning · 2023
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Re-examining calibration: The case of question answering
Chenglei Si, Chen Zhao, Sewon Min, and Jordan Boyd-Graber · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
BIG bench authors · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
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Cohere-command models, 2023
Cohere · 2023
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Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston · 2023
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Improving factuality and reasoning in language models through multiagent debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch · 2023
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Simple synthetic data reduces sycophancy in large language models
Jerry Wei, Da Huang, Yifeng Lu, Denny Zhou, and Quoc V Le · 2023
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Examining the inter-consistency of large language models: An in-depth analysis via debate
Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, and Bing Qin · 2023
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Generate rather than retrieve: Large language models are strong context generators
Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang · 2023
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Navigating the grey area: How expressions of uncertainty and overconfidence affect language models
Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto · 2023
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Litcab: Lightweight language model calibration over short- and long-form responses
Xin Liu, Muhammad Khalifa, and Lu Wang · 2024
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2024
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2024
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Reflexion: Language agents with verbal reinforcement learning
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Adaplanner: Adaptive planning from feedback with language models
Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang · 2024
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