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Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty.
The Population Frequences of Species and the Estimation of Population Parameters
I. J. Good. 1953 · 1953
Earlier work this paper cites.
How to Do Things with Words
J. L. Austin. 1962 · 1962
Earlier work this paper cites.
On the uniform convergence of relative frequencies of events to their probabilities
Vladimir N Vapnik and A Ya Chervonenkis. 1971 · 1971
Earlier work this paper cites.
Logic and Conversation
H. P. Grice. 1975 · 1975
Earlier work this paper cites.
The Well-Calibrated Bayesian
A. P. Dawid. 1982 · 1982
Earlier work this paper cites.
On the Method of Bounded Differences
Colin McDiarmid. 1989 · 1989
Earlier work this paper cites.
Toward efficient agnostic learning
Michael J. Kearns, Robert E. Schapire, and Linda M. Sellie. 1994 · 1994
Earlier work this paper cites.
An Introduction to Computational Learning Theory
Michael J. Kearns and Umesh V. Vazirani. 1994 · 1994
Earlier work this paper cites.
On the Convergence Rate of Good–Turing Estimators. In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000) . Morgan Kaufmann, Palo Alto, California, USA, 1–6
David A. McAllester and Robert E. Schapire. 2000 · 2000
Earlier work this paper cites.
Foundations of Cryptography: Volume 1, Basic Tools
Oded Goldreich. 2001 · 2001
Earlier work this paper cites.
Probabilistic and on-line methods in machine learning
Adam Kalai. 2001 · 2001
Earlier work this paper cites.
Concentration inequalities for the missing mass and for histogram rule error
David McAllester and Luis Ortiz. 2003 · 2003
Earlier work this paper cites.
Dataset Shift in Machine Learning
Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence (Eds.). 2009 · 2009
Earlier work this paper cites.
A Few Useful Things to Know About Machine Learning
Pedro Domingos. 2012 · 2012
Earlier work this paper cites.
A unifying view on dataset shift in classification
José G. Moreno-Torres, Troy Raeder, Rocío Alaiz-Rodríguez, Nitesh V. Chawla, and Francisco Herrera. 2012 · 2012
Earlier work this paper cites.
Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015 · 2015
Earlier work this paper cites.
Learning Reductions That Really Work
Alina Beygelzimer, Hal Daumé III, John Langford, and Paul Mineiro. 2016 · 2015
Earlier work this paper cites.
Actively Avoiding Nonsense in Generative Models. In Proceedings of the 31st Conference on Learning Theory (Proceedings of Machine Learning Research, Vol. 75) , Sébastien Bubeck, Vianney Perchet, and Philippe Rigollet (Eds.). PMLR, Stockholm, Sweden, 209–227
Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, and Christos Tzamos. 2018 · 2018
Earlier work this paper cites.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 9459–9474
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
Earlier work this paper cites.
On Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL) . Association for Computational Linguistics, Online, 1906–1919
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 2020
Earlier work this paper cites.
Artificial Intelligence: A Modern Approach (4 ed.)
Stuart J. Russell and Peter Norvig. 2020 · 2020
Earlier work this paper cites.
Measuring Mathematical Problem Solving with the MATH Dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Earlier work this paper cites.
Investigating Memorization of Conspiracy Theories in Text Generation. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 . Association for Computational Linguistics, Online, 4718–4729
Sharon Levy, Michael Saxon, and William Yang Wang. 2021 · 2021
Earlier work this paper cites.
WebGPT: Browser-Assisted Question-Answering with Human Feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. 2021 · 2021
Earlier work this paper cites.
Retrieval Augmentation Reduces Hallucination in Conversation. In Findings of the Association for Computational Linguistics: EMNLP 2021 . Association for Computational Linguistics, Punta Cana, Dominican Republic, 3784–3803
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021
Earlier work this paper cites.
Constitutional AI: Harmlessness from AI Feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. 2022 · 2022
Earlier work this paper cites.
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 B. Brown, Jack Clark, Nicholas Joseph, Benjamin Mann, Sam McCandlish, Chris Olah, and Jared Kaplan. 2022 · 2022
Earlier work this paper cites.
Factuality Enhanced Language Models for Open-Ended Text Generation
Nayeon Lee, Wei Ping, Peng Xu, Mostofa Patwary, Pascale Fung, Mohammad Shoeybi, and Bryan Catanzaro. 2022 · 2022
Earlier work this paper cites.
How Pre-Trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis
Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, and Qun Liu. 2022 · 2022
Earlier work this paper cites.
Teaching Models to Express Their Uncertainty in Words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022a · 2022
Earlier work this paper cites.
TruthfulQA: Measuring How Models Mimic Human Falsehoods. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Dublin, Ireland, 3214–3252
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022b · 2022
Earlier work this paper cites.
Reducing Conversational Agents’ Overconfidence Through Linguistic Calibration
Sabrina J. Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau. 2022 · 2022
Cited alongside, same era.
Training Language Models to Follow Instructions with Human Feedback. In Advances in Neural Information Processing Systems , Vol. 35. 27730–27744
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Cited alongside, same era.
BBQ: A hand-built bias benchmark for question answering. In Findings of the Association for Computational Linguistics: ACL 2022 , Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds.). Association for Computational Linguistics, Dublin, Ireland, 2086–2105
Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel Bowman. 2022 · 2022
Cited alongside, same era.
GPT4All: Training an Assistant-Style Chatbot with Large-Scale Data Distillation from GPT-3.5-Turbo
Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt, and Andriy Mulyar. 2023 · 2023
GPQA: A Graduate-Level Google-Proof Q&A Benchmark. In Proceedings of the 1st Conference on Language Modeling (COLM 2024)
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. 2024 · 2024
Later among the works it cites.
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Zayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, and Greg Durrett. 2024 · 2024
Later among the works it cites.
Fine–Tuning Language Models for Factuality. In Proceedings of the Twelfth International Conference on Learning Representations (ICLR 2024) . Vienna, Austria
Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher D. Manning, and Chelsea Finn. 2024 · 2024
Later among the works it cites.
Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue, and Wenhu Chen. 2024 · 2024
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Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
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Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Delong Chen, Wenliang Dai, Ho Shu Chan, Andrea Madotto, and Pascale Fung. 2023 · 2023
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SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 9004–9017
Potsawee Manakul, Adian Liusie, and Mark Gales. 2023 · 2023
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Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
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