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Aligning AI systems to users' interests requires understanding and incorporating humans' complex values and preferences.
Fine-tuning language models from human preferences
Daniel M. Ziegler, Nisan Stiennon, Jeff Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019 · 1909
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Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E. Terry. 1952 · 1952
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Efficient experimental design with marketing research applications
Warren F. Kuhfeld, Randall D. Tobias, and Mark Garratt. 1994 · 1994
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Heterogeneous Uncertainty Sampling for Supervised Learning
David D. Lewis and Jason Catlett. 1994 · 1994
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A note on linear utility
Juan Carlos Candeal-Haro and Esteban Induráin-Eraso. 1995 · 1995
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Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments
Neeraj Arora and Joel Huber. 2001 · 2001
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Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application
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A tutorial on particle filtering and smoothing: Fifteen years later
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Adaptive design optimization: A mutual information-based approach to model discrimination in cognitive science
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Contextual multi-armed bandits
Tyler Lu, David Pal, and Martin Pal. 2010 · 2010
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Active learning literature survey
Burr Settles. 2010 · 2010
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. 2011 · 2011
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Preference Learning , pages 2669–2672. Springer US, Boston, MA
Johannes Fürnkranz and Eyke Hüllermeier. 2012 · 2012
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A bayesian approach to targeted experiment design
Joep Vanlier, Christian Tiemann, Peter Hilbers, and Natal van Riel. 2012 · 2012
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Deep Reinforcement Learning from Human Preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
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Particle filters: A hands-on tutorial
Jos Elfring, Elena Torta, and René van de Molengraft. 2021 · 2021
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Robust active preference elicitation
Phebe Vayanos, Yingxiao Ye, Duncan McElfresh, John Dickerson, and Eric Rice. 2021 · 2021
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Inferring rewards from language in context
Jessy Lin, Daniel Fried, Dan Klein, and Anca Dragan. 2022 · 2022
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Interactively learning preference constraints in linear bandits
David Lindner, Sebastian Tschiatschek, Katja Hofmann, and Andreas Krause. 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 Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Prolific.ac—a subject pool for online experiments
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Active preference-based learning of reward functions
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Optimal bayesian design for discriminating between models with intractable likelihoods in epidemiology
Mahasen B. Dehideniya, Christopher C. Drovandi, and James M. McGree. 2018 · 2018
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Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, and Noah Goodman. 2019 · 2019
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Active ranking from pairwise comparisons and when parametric assumptions do not help
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Human few-shot learning of compositional instructions
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Eliciting human preferences with language models
Belinda Z. Li, Alex Tamkin, Noah Goodman, and Jacob Andreas. 2023 · 2023
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Decision-oriented dialogue for human-ai collaboration
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OpenAI. 2023 · 2023
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Active preference inference using language models and probabilistic reasoning
Top Piriyakulkij, Volodymyr Kuleshov, and Kevin Ellis. 2023 · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
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Modern bayesian experimental design
Tom Rainforth, Adam Foster, Desi R Ivanova, and Freddie Bickford Smith. 2023 · 2023
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Task ambiguity in humans and language models
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Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D. Manning. 2023 · 2023
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Kto: Model alignment as prospect theoretic optimization
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