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As natural language becomes the default interface for human-AI interaction, there is a need for LMs to appropriately communicate uncertainties in downstream applications.
Language Models are Few-Shot Learners
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Hedges: A study in meaning criteria and the logic of fuzzy concepts
George Lakoff. 1975 · 1975
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Discourse variation and hedging
Karin Aijmer. 1986 · 1986
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Decisions based on numerically and verbally expressed uncertainties
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Marek J Druzdzel. 1989 · 1989
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Semantics and pragmatics of hedges in English and Japanese
Reiko Itani. 1995 · 1995
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Subjectivity and certainty in epistemic modality: A study of dutch epistemic modifiers
José Sanders and Wilbert Spooren. 1996 · 1996
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Measuring psychological uncertainty: Verbal versus numeric methods
Paul D Windschitl and Gary L Wells. 1996 · 1996
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Austin S Babrow, Chris R Kasch, and Leigh A Ford. 1998 · 1998
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Hedges in Japanese conversation: The influence of age, sex, and formality
Shizuka Lauwereyns. 2002 · 2002
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Stance and engagement: A model of interaction in academic discourse
Ken Hyland. 2005 · 2005
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Constructing grounded theory: A practical guide through qualitative analysis
Kathy Charmaz. 2006 · 2006
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Determining the role of hedging devices in the political discourse of two american presidentiables in 2008
Fahad Al-Rashady. 2012 · 2008
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Most people are not WEIRD
Joseph Henrich, Steven J. Heine, and Ara Norenzayan. 2010 · 2010
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Understanding pragmatic markers: A variational pragmatic approach
Karin Aijmer. 2013 · 2013
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Reporting bias and knowledge acquisition
Jonathan Gordon and Benjamin Van Durme. 2013 · 2013
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Disciplinary discourses: Writer stance in research articles
Ken Hyland. 2014 · 2014
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Hedging strategies in academic discourse: a comparative analysis of Turkish writers and native writers of English
Oktay Yagız and Cuneyt Demir. 2014 · 2014
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The role of explanations on trust and reliance in clinical decision support systems
Adrian Bussone, Simone Stumpf, and Dympna O’Sullivan. 2015 · 2015
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Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err
Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015 · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht. 2015 · 2015
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Pragmatic Language Interpretation as Probabilistic Inference
Noah D. Goodman and Michael C. Frank. 2016 · 2016
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How much information?: Effects of transparency on trust in an algorithmic interface
René F. Kizilcec. 2016 · 2016
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Are distributional representations ready for the real world? evaluating word vectors for grounded perceptual meaning
Li Lucy and Jon Gauthier. 2017 · 2017
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
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A corpus-based study on cross-cultural divergence in the use of hedges in academic research articles written by vietnamese and native english-speaking authors
Thu Nguyen Thi Thuy. 2018 · 2018
Cited alongside, same era.
The#benderrule: On naming the languages we study and why it matters
Emily Bender. 2019 · 2019
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Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making
Carrie J. Cai, Emily Reif, Narayan Hegde, Jason D. Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda B. Viégas, Gregory S. Corrado, Martin C. Stumpe, and Michael Terry. 2019 · 2019
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Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika. 2019 · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
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Teaching models to express their uncertainty in words
Stephanie C. Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E. Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Francis Christiano, Jan Leike, and Ryan J. Lowe. 2022 · 2022
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Neural theory-of-mind? on the limits of social intelligence in large LMs
Maarten Sap, Ronan Le Bras, Daniel Fried, and Yejin Choi. 2022a · 2022
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Annotators with attitudes: How annotator beliefs and identities bias toxic language detection
Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, and Noah A. Smith. 2022b · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
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