Browse 8 peer-reviewed papers from University Of Southern California spanning Large Language Models, Reinforcement Learning from Human Feedback (RLHF) (2023–2025). Research powered by Prolific's high-quality participant data.
This page lists 8 peer-reviewed papers from researchers at University Of Southern California in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: K Zhou, JD Hwang, X Ren, N Dziri
Year: 2025
Published in: Proceedings of the ..., 2025 - aclanthology.org
Institution: Stanford University, University of Southern California, Carnegie Mellon University, Allen Institute for AI
Research Area: Human-LM Reliance, Interaction-Centered Framework, Human-Computer Interaction
Discipline: Human-Computer Interaction, Artificial Intelligence
The study introduces Rel-A.I., an interaction-centered evaluation approach to measure human reliance on LLM responses, revealing that politeness and interaction context significantly influence user reliance.
Methods: Nine user studies were conducted, analyzing user reliance influenced by LLM communication features such as politeness and context through participant interaction experiments.
Key Findings: The degree of human reliance on LLM responses based on communication style (e.g., politeness) and interaction context (e.g., knowledge domain, prior interactions).
Citations: 18
Sample Size: 450
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Authors: S Carney, I Riveros, S Tully
Year: 2025
Published in: Available at SSRN 4988760, 2025 - papers.ssrn.com
Institution: University of Southern California
Research Area: Consumer Engagement with AI Disclosures, Social Media Marketing, Social Psychology
Discipline: Social Science
AI-generated content disclosures on social media reduce consumer engagement primarily due to a decrease in parasocial connections, as users perceive creators to exert less effort; signaling greater effort can mitigate this effect.
Methods: Analysis of TikTok engagement data following AIGC disclosure implementation, supplemented by six preregistered experiments.
Key Findings: Impact of AIGC disclosures on consumer engagement and the mediating role of parasocial connections.
Citations: 6
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Authors: Z Cheng, J You
Year: 2025
Published in: arXiv preprint arXiv:2509.22989, 2025 - arxiv.org
Institution: University of Southern California, University of California Berkeley
Research Area: Artificial Intelligence, Computer Science, Computer Science and Game Theory, Strategic Persuasion, Reinforcement Learning, Language Models, Large Language Models, Reinforcement Learning from Human Feedback (RLHF)
Discipline: Artificial Intelligence
This paper introduces a scalable framework, utilizing Bayesian Persuasion, to evaluate and train LLMs for strategic persuasion, demonstrating significant persuasion gains and effective strategies through reinforcement learning.
Methods: Repurposed human-human persuasion datasets for evaluation and training; applied Bayesian Persuasion framework; used reinforcement learning to optimize LLMs for strategic persuasion.
Key Findings: The persuasive capabilities and strategies of large language models (LLMs) in various settings.
Citations: 1
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Authors: Y Yin, N Jia, CJ Wakslak
Year: 2024
Published in: Proceedings of the National Academy of ..., 2024 - pnas.org
Institution: University of Southern California Los Angeles
Research Area: Human-AI Interaction, Social Perception of AI, Media Effects
Discipline: Social Science
AI responses make people feel more heard and are better at emotional support compared to humans, but labeling responses as AI diminishes this effect.
Methods: Experiment and follow-up study to assess recipient reactions to AI vs. human-generated responses and determine emotional support efficacy.
Key Findings: The degree to which recipients feel heard, emotion detection accuracy, and third-party ratings of emotional support quality.
DOI: https://doi.org/10.1073/pnas.2319112121
Citations: 201
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Authors: D Guilbeault, S Delecourt, T Hull, BS Desikan, M Chu
Year: 2024
Published in: Nature, 2024 - nature.com
Institution: University of California Berkeley, Institute For Public Policy Research, Columbia University, University of Southern California Los Angeles
Research Area: Gender Bias, Computational Social Science, Online Media, AI Bias
Discipline: Computational Social Science
Online images significantly amplify gender bias compared to text, with biases in visual content impacting societal beliefs about gender roles.
Methods: Analyzed 3,495 social categories using over one million images from platforms like Google, Wikipedia, and IMDb, compared visual content to billions of words from the same platforms, and conducted a preregistered national experiment to assess the psychological impact on participants' beliefs.
Key Findings: The prevalence and psychological impact of gender bias in online images compared to text, including gender associations and representation disparities.
DOI: https://doi.org/10.1038/s41586-024-07068-x
Citations: 72
Sample Size: 3495
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Authors: K Zhou,JD Hwang, X Ren,M Sap
Year: 2024
Published in: ArXiv
Institution: Allen Institute for AI, Carnegie Mellon University, Stanford University, University of Southern California
Research Area: LLM Reliability and Uncertainty Quantification, Reinforcement Learning from Human Feedback (RLHF), Large Language Models
Discipline: Artificial Intelligence
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Authors: G Gui, O Toubia
Year: 2023
Published in: arXiv preprint arXiv:2312.15524, 2023 - arxiv.org
Institution: University of Southern California, Columbia Business School
Research Area: LLMs and Causal Inference in Human Behavior Simulation, Large Language Models
Discipline: Artificial Intelligence, Information Retrieval (cs.IR), Econometrics (econ.EM), Applications (stat.AP)
Citations: 76
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Authors: E Scarpulla, MD Stosic, AE Weaver
Year: 2023
Published in: Frontiers in ..., 2023 - frontiersin.org
Institution: University of San Francisco, University of Southern California, University of Nevada, Las Vegas
Research Area: Social Media, Emotion Recognition, Mental Health
Discipline: Psychology
DOI: https://doi.org/10.3389/fpsyg.2023.1161300
Citations: 7
