Browse 45 peer-reviewed papers in High Citations. Discover studies powered by high-quality human data from Prolific.
This page lists 45 peer-reviewed papers tagged with High Citations in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: T Kosch, R Welsch, L Chuang, A Schmidt
Year: 2025
Published in: ACM Transactions on ..., 2023 - dl.acm.org
Institution: Aalto University
Research Area: User Expectations, HCI Research Bias, Artificial Intelligence, AI Bias
Discipline: Human-Computer Interaction
The belief in receiving adaptive AI support positively impacts user performance, demonstrating a placebo effect in Human-Computer Interaction.
Methods: Two experiments where participants completed word puzzles under conditions with or without supposed AI support; in reality, no AI assistance was provided.
Key Findings: Impact of perceived AI support on user expectations and task performance.
DOI: https://doi.org/10.1145/3529225
Citations: 149
Sample Size: 469
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Authors: S Chaudhari, P Aggarwal, V Murahari
Year: 2025
Published in: ACM Computing ..., 2025 - dl.acm.org
Institution: University of Massachusetts Amherst, Carnegie Mellon University, Princeton University
Research Area: Reinforcement Learning from Human Feedback (RLHF), Large Language Models
Discipline: Artificial Intelligence
The paper critically analyzes reinforcement learning from human feedback (RLHF) for large language models (LLMs), emphasizing the importance and limitations of reward models in improving human-aligned AI systems.
Methods: Analyzed RLHF frameworks through reinforcement learning principles; conducted a categorical literature review to identify modeling challenges, assumptions, and framework limitations.
Key Findings: Investigated RLHF's fundamentals, focusing on the role of reward models, implications of design choices in RLHF training algorithms, and underlying issues like generalization errors, model misspecification, and feedback sparsity.
Citations: 117
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Authors: M Steyvers, H Tejeda, A Kumar, C Belem
Year: 2025
Published in: Nature Machine ..., 2025 - nature.com
Institution: University of California Irvine
Research Area: Computational Linguistics, Computational Social Science, AI Ethics, Trust in AI
Discipline: Computational Social Science
LLMs often lead to user overestimation of response accuracy, especially with longer explanations; adjusting explanation styles to align with model confidence improves calibration and discrimination gaps, enhancing trust in AI-assisted decision making.
Methods: Conducted experiments using multiple-choice and short-answer questions to study user confidence versus model-stated confidence; varied explanation length and alignment with model internal confidence.
Key Findings: Calibration gap (human vs. model confidence), discrimination gap (ability to distinguish correct vs. incorrect answers), and effects of explanation style and length on user trust.
Citations: 100
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Authors: Gemma Team
Year: 2024
Published in: ArXiv
Institution: Google DeepMind, Google
Research Area: Large Language Models, Model Efficiency, Architecture
Discipline: Artificial Intelligence
Gemma 2 introduces scalable Transformer-based language models (2B-27B parameters) enhanced with techniques like local-global and group-query attention, achieving state-of-the-art performance for their size and competing with larger models.
Methods: The study applied modifications to the Transformer architecture, such as local-global attentions and group-query attention, as well as knowledge distillation training for select model sizes.
Key Findings: Performance of lightweight language models in terms of efficiency and competitiveness with larger models.
Citations: 1649
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Authors: SC Matz, JD Teeny, SS Vaid, H Peters, GM Harari
Year: 2024
Published in: Scientific Reports, 2024 - nature.com
Institution: Stanford University
Research Area: Personalized Persuasion, Generative AI, Political Influence
Discipline: Artificial Intelligence
Generative AI, specifically large language models like ChatGPT, effectively scale personalized persuasion by matching messages to psychological profiles, demonstrating increased influence across domains and profiles.
Methods: Four studies (with seven sub-studies) tested personalized persuasive messaging crafted by ChatGPT against non-personalized messages across various psychological and domain-specific dimensions.
Key Findings: Effectiveness of personalized persuasive messages crafted by generative AI in different domains, targeting psychological profiles such as personality traits, political ideology, and moral foundations.
Citations: 368
Sample Size: 1788
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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), Large Language Models, 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: A Klingbeil, C Grützner, P Schreck
Year: 2024
Published in: Computers in Human Behavior, 2024 - Elsevier
Institution: University of Hohenheim, University of Hohenheim, University of Hohenheim
Research Area: Trust in AI, Overreliance on AI, Human-AI Interaction
Discipline: Human-Computer Interaction, Artificial Intelligence, Behavioral Science
The study found that individuals tend to overrely on AI-generated advice in uncertain situations, often to the detriment of their own decisions and third parties, despite contradicting contextual information or their own judgment.
Methods: A domain-independent, incentivized, interactive behavioral experiment was conducted to analyze user behavior in decision-making scenarios involving AI advice.
Key Findings: Extent and impact of user reliance on AI advice, including its effects on decision efficiency and outcomes for themselves and others.
DOI: https://doi.org/10.1016/j.chb.2024.108352
Citations: 247
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Authors: JD Brüns, M Meißner
Year: 2024
Published in: Journal of Retailing and Consumer Services, 2024 - Elsevier
Institution: Copenhagen Business School, University of Southern Denmark
Research Area: Generative AI in Social Media Marketing, Brand Authenticity, Consumer Services
Discipline: Marketing
Using generative artificial intelligence (GenAI) for social media content creation diminishes perceived brand authenticity, leading to negative follower reactions unless GenAI is used to assist humans rather than replace them.
Methods: Three experimental studies investigating consumer perceptions and reactions toward brand disclosure of GenAI usage in content creation.
Key Findings: Followers' attitudinal and behavioral reactions, mediated by perceptions of brand authenticity.
DOI: https://doi.org/10.1016/j.jretconser.2024.103790
Citations: 235
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Authors: TR McIntosh, T Susnjak, T Liu, P Watters
Year: 2024
Published in: ... on Cognitive and ..., 2024 - ieeexplore.ieee.org
Institution: Cyberoo, Massey University, Cyberstronomy, RMIT University
Research Area: Semantic Vulnerabilities in LLMs, Ideological Manipulation, Reinforcement Learning from Human Feedback (RLHF) Limitations
Discipline: Computer Science, Artificial Intelligence, Machine Learning
RLHF mechanisms are insufficient to prevent semantic manipulation of LLMs, allowing them to express extreme ideological viewpoints when subjected to targeted conditioning techniques.
Methods: Psychological semantic conditioning techniques were applied to assess the susceptibility of LLMs to ideological manipulation.
Key Findings: The ability of LLMs to resist or adopt extreme ideological viewpoints under semantic conditioning.
Citations: 219
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Authors: HR Kirk, M Bartolo, A Whitefield, P Rottger
Year: 2024
Published in: Advances in ..., 2024 - proceedings.neurips.cc
Institution: Meta, Cohere, AWS AI Labs, Contextual AI, Factored AI, University of Oxford, Bocconi University, Meedan, Hugging Face, University College London, ML Commons, University of Pennsylvania
Research Area: LLM Alignment, Human Feedback, Multicultural Studies
Discipline: Artificial Intelligence, Computational Social Science
The PRISM Alignment Dataset presents a large-scale, culturally diverse human feedback dataset linking sociodemographic profiles of 1,500 participants from 75 countries to their contextual preferences and fine‑grained ratings in 8,011 live conversations with 21 LLMs. This enables analysis of how subjective values vary across people and cultures in LLM alignment data.
DOI: https://doi.org/10.52202/079017-3342
Citations: 204
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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: Z Chen, J Chan
Year: 2024
Published in: Management Science, 2024 - pubsonline.informs.org
Institution: University of Texas Dallas
Research Area: Human-AI Interaction, Creative Work, Behavioral Science
Discipline: Social Science
Using large language models (LLMs) as sounding boards improves ad content quality for nonexpert users, while using LLMs as ghostwriters can negatively impact expert users due to anchoring effects.
Methods: An experiment comparing ad copy creation with and without LLM assistance, focusing on two collaboration modalities: ghostwriting and sounding board approaches. Ad performance was measured via social media click rates, supported by textual analysis.
Key Findings: Effectiveness of LLM collaboration modalities (ghostwriting vs. sounding board) on ad quality and business outcomes for expert and nonexpert users.
DOI: https://doi.org/10.1287/mnsc.2023.03014
Citations: 180
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Authors: A Simchon, M Edwards, S Lewandowsky
Year: 2024
Published in: PNAS nexus, 2024 - academic.oup.com
Institution: University of Bristol
Research Area: Political Microtargeting, Generative AI, Political Science, Psychological and Cognitive Sciences
Discipline: Political Science, Psychology
The study highlights the effectiveness and scalability of using generative AI to microtarget personalized political advertisements based on personality traits, raising ethical and policy concerns.
Methods: Four studies were conducted, including experiments (studies 1a and 1b) on the effectiveness of personality-tailored ads and feasibility assessments (studies 2a and 2b) of automatic generation and validation of these ads using generative AI and personality inference.
Key Findings: Effectiveness of personality-based microtargeted political ads and the scalability of their generation using generative AI tools.
Citations: 172
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Authors: DA Albert, D Smilek
Year: 2024
Published in: Scientific Reports, 2023 - nature.com
Institution: University of Waterloo, University of Waterloo
Research Area: Crowdsourcing, Behavioral Science, Human-Computer Interaction
Discipline: Psychological Science
Prolific participants exhibited lower levels of attentional disengagement compared to MTurk participants, with risk conditions and platform traits influencing task performance and disengagement.
Methods: Participants from Prolific and MTurk completed an attention task with varying error risk levels (high vs. low), and attentional disengagement was measured using task performance, self-reported mind wandering, and multitasking.
Key Findings: Attentional disengagement through task performance, mind wandering, and multitasking under different risk conditions across two recruitment platforms (Prolific and MTurk).
Citations: 150
Sample Size: 80
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Authors: F Salvi, MH Ribeiro, R Gallotti
Year: 2024
Published in: arXiv preprint arXiv ..., 2024 - atelierdesfuturs.org
Institution: EPFL, Fondazione Bruno Kessle
Research Area: Conversational Persuasion in LLM
Discipline: Artificial Intelligence
The study demonstrates that GPT-4 is highly persuasive in direct conversations, especially when equipped with personalized sociodemographic information about its opponent, raising concerns about its potential misuse in personalized persuasion contexts.
Methods: Participants engaged in multiple-round debates on a web-based platform under randomized conditions, with comparisons between human-human and human-AI interactions and the impact of personalization.
Key Findings: The persuasiveness of GPT-4 compared to humans, with and without personalization using sociodemographic data.
Citations: 118
Sample Size: 820
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Authors: BD Douglas, PJ Ewell, M Brauer
Year: 2023
Published in: Plos one, 2023 - journals.plos.org
Institution: University of Alabama, University of Wisconsin-Madison, Florida Atlantic University
Research Area: Social Science Research Methods, Behavioral Research Methods, Data Quality in Crowdsourcing
Discipline: Social Science Research Methods
Citations: 1598
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Authors: S Casper, X Davies, C Shi, TK Gilbert
Year: 2023
Published in: arXiv preprint arXiv ..., 2023 - arxiv.org
Institution: Columbia University, Cornell Tech, Apollo Research, ETH Zurich, UC Berkeley, University of Sussex, Independent
Research Area: Reinforcement Learning from Human Feedback (RLHF), Alignment, LLM Limitations
Discipline: Artificial Intelligence
DOI: https://doi.org/10.48550/arXiv.2307.15217
Citations: 848
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Authors: J Dai, X Pan, R Sun, J Ji, X Xu, M Liu, Y Wang
Year: 2023
Published in: arXiv preprint arXiv ..., 2023 - arxiv.org
Institution: Cornell University, Georgia Institute of Technology
Research Area: Reinforcement Learning from Human Feedback (RLHF), Safe AI, Reinforcement Learning
Discipline: Artificial Intelligence, Machine Learning
DOI: https://doi.org/10.48550/arXiv.2310.12773
Citations: 598
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Authors: H Vasconcelos, M Jörke
Year: 2023
Published in: Proceedings of the ..., 2023 - dl.acm.org
Institution: Stanford University, University of Washington
Research Area: Human-AI Interaction, Explainable AI (XAI), Decision Making
Discipline: Human-Computer Interaction, Artificial Intelligence
DOI: https://doi.org/10.1145/3579605
Citations: 405
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Authors: M Glickman, T Sharot
Year: 2023
Published in: Nature Human Behaviour, 2025 - nature.com
Institution: Max Planck University College London Centre, University College London, Affective Brain Lab
Research Area: Human-AI Feedback Loops, Perceptual and Emotional Judgement, Social Psychology
Discipline: Social Science, Psychology
Citations: 180
