Browse 7 peer-reviewed papers from Duke University spanning Experimental Survey Research Methodology, Reinforcement Learning from Human Feedback (RLHF) (2023–2025). Research powered by Prolific's high-quality participant data.
This page lists 7 peer-reviewed papers from researchers at Duke University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: D Jordan, T Ollerenshaw, A Trexler
Year: 2025
Published in: 2025 - weekendu.uh.edu
Institution: University of Houston, Duke University
Research Area: Experimental Survey Research Methodology
Discipline: Social Science, Research Methodology
Repeated measure designs offer enhanced precision with minimal bias, suitable for various experiments despite slight attenuation of treatment effects.
Methods: Experimentally manipulated six classic political science experiments across three sample types, including extensions with proximity manipulation and sample-type variations.
Key Findings: Suitability and precision of repeated measure designs in survey experiments, including treatment effect estimations and design applicability across different sample types and methodologies.
Citations: 1
Sample Size: 13163
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Authors: T Kaufmann, P Weng, V Bengs, E Hüllermeier
Year: 2024
Published in: 2024 - epub.ub.uni-muenchen.de
Institution: Paderborn University, German Research Center for Artificial Intelligence (DFKI), Duke Kunshan University
Research Area: Reinforcement Learning from Human Feedback (RLHF), LLM, Reward Modeling
Discipline: Artificial Intelligence
This paper surveys the fundamentals, diverse applications, and evolving impact of reinforcement learning from human feedback (RLHF), emphasizing its role in improving intelligent system alignment and performance.
Methods: The paper utilizes a survey-based approach to synthesize existing research, exploring the interactions between reinforcement learning algorithms and human input.
Key Findings: The study examines the principles, dynamics, applications, and trends in RLHF, offering insights into its role in enhancing large language models (LLMs) and intelligent systems.
Citations: 354
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Authors: DJ Kravitz, SR Mitroff
Year: 2024
Published in: Policy Insights from ..., 2024 - journals.sagepub.com
Institution: University of North Carolina at Chapel Hill, Duke University
Research Area: Crowdsourcing, Behavioral Sciences, Policy Applications
Discipline: Behavioral Science
Citations: 2
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Authors: Eyal Aharoni, Sharlene Fernandes, Daniel J. Brady, Caelan Alexander, Michael Criner, Kara Queen, Javier Rando, Eddy Nahmias, Victor Crespo
Year: 2024
Published in: Nature
Institution: Duke University, ETH Zurich, Georgia State University
Research Area: Moral Responsibility, Agency in AI, Human-AI Moral Interaction
Discipline: Artificial Intelligence Ethics
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Authors: Yizhe Zhang, Yucheng Jin, Li Chen, Ting Yang
Year: 2024
Published in: ArXiv
Institution: Department of Computer Science China, Duke Kunshan University, Hong Kong Baptist University
Research Area: User Experience (UX), Conversational AI, Recommender Systems
Discipline: Computer Science
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Authors: Jing-Jing Li♡♠ Valentina Pyatkin♠ Max Kleiman-Weiner♣ Liwei Jiang♣ Nouha Dziri♠ &Anne G. E. Collins♡ Jana Schaich Borg♢ Maarten Sap♠◆ Yejin Choi♣ Sydney Levine♠
Year: 2024
Published in: ArXiv
Institution: Allen Institute for AI, Duke University, University of California Berkeley, University of Washington
Research Area: LLM Safety Moderation, Interpretable AI (XAI), LLM Alignment, Steerable AI
Discipline: Artificial Intelligence
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Authors: JA Reif, RP Larrick, JB Soll
Year: 2023
Published in: Proceedings of the National Academy of ..., 2025 - pnas.org
Institution: Fuqua School of Business, Duke University
Research Area: Workplace AI, Psychology, AI Ethics
Discipline: Social Science
Citations: 24