Browse 12 peer-reviewed papers from Columbia University spanning Human-AI Collaboration, LLM Limitations (2020–2025). Research powered by Prolific's high-quality participant data.
This page lists 12 peer-reviewed papers from researchers at Columbia University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: G Beknazar-Yuzbashev, R Jiménez-Durán, J McCrosky
Year: 2025
Published in: 2025 - econstor.eu
Institution: Mozilla Foundation, Columbia University, Bocconi University, Stanford University, University of Warwick
Research Area: Social Media, User Engagement, Toxicity
Discipline: Social Science
Reducing exposure to toxic content on social media lowers user engagement but also decreases the toxicity of user-generated content, highlighting a trade-off for platforms between reduced toxicity and increased engagement.
Methods: Pre-registered browser extension field experiment on Facebook, Twitter, and YouTube to randomly hide toxic content for six weeks; supplemented with a survey experiment.
Key Findings: Impact of reduced exposure to toxic content on advertising impressions, time spent, engagement, and user-generated content toxicity; explored curiosity and alignment between engagement and welfare.
Citations: 76
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Authors: HCB Huang
Year: 2025
Published in: Journal of Experimental Psychology: General, 2025 - psycnet.apa.org
Institution: University of British Columbia
Research Area: Human-AI Collaboration, Creativity, Experimental Psychology
Discipline: Experimental Psychology
Moderate levels of human-AI collaboration enhance creative performance due to increased knowledge diversity, but excessive or minimal involvement diminishes this effect.
Methods: Two experiments assigned 139 business professionals and 319 working adults to collaborate with ChatGPT at varying levels, and a follow-up survey among 188 creative industry workers was conducted to replicate findings.
Key Findings: The impact of varying degrees of human-AI collaboration on creative performance, evaluated by human judges, entrepreneurs, and AI metrics.
Citations: 3
Sample Size: 646
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Authors: Y Ba, MV Mancenido, EK Chiou, R Pan
Year: 2025
Published in: Behavior Research Methods, 2025 - Springer
Institution: University of Delaware, National Taiwan University, University of British Columbia, Monash University
Research Area: Crowdsourcing, Data Quality, Spamming Behavior Detection, LLM Applications in Behavioral Research
Discipline: Computer Science, Artificial Intelligence, LLM
The paper introduces a systematic method to evaluate crowdsourced data quality and detect spam behaviors through variance decomposition, proposing a spammer index and credibility metrics to improve consistency and reliability in labeling tasks.
Methods: Variance decomposition, Markov chain models, and generalized random effects models were used to assess annotator consistency and credibility; metrics were applied to both simulated and real-world data from two crowdsourcing platforms.
Key Findings: Quality of crowdsourced data, spammer behaviors, annotators’ consistency, and credibility.
Citations: 2
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Authors: O Jacobs
Year: 2025
Published in: 2025 - open.library.ubc.ca
Institution: University of British Columbia
Research Area: Mind Perception in Human-AI Interaction, Anthropomorphism, Psychology
Discipline: Psychology, Human-Computer Interaction (HCI) in AI
This is a University of British Columbia doctoral thesis that investigates how people perceive and attribute mental states (beliefs, intentions, minds) to artificial intelligence systems — exploring the psychological and conceptual underpinnings of mind perception in human–AI interaction.
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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: S Kapoor, N Gruver, M Roberts
Year: 2024
Published in: Advances in ..., 2024 - proceedings.neurips.cc
Institution: Abacus AI, University of Cambridge, New York University, Columbia University
Research Area: Uncertainty Estimation, LLM Limitations, Know-What-You-Don't-Know, Computational Cognition
Discipline: Artificial Intelligence
Fine-tuning large language models (LLMs) on a small dataset of graded examples improves uncertainty estimations, enhancing their applicability in high-stakes scenarios and human-AI collaboration.
Methods: The researchers fine-tuned LLMs using a small dataset of graded correct and incorrect answers with LoRA (Low-Rank Adaptation) to create uncertainty estimates and conducted a user study to investigate their utility in human-AI collaboration.
Key Findings: Calibration and generalization of uncertainty estimates, performance of fine-tuning LLMs for uncertainty estimation, and human-AI interaction improvements informed by uncertainty data.
Citations: 71
Sample Size: 1000
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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: 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, LLM
Discipline: Artificial Intelligence (cs.AI), Information Retrieval (cs.IR), Econometrics (econ.EM), Applications (stat.AP)
Citations: 76
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Authors: K Stanton, RW Carpenter, M Nance
Year: 2022
Published in: Experimental and ..., 2022 - psycnet.apa.org
Institution: University of Missouri-Columbia, University of Texas at Austin
Research Area: Psychometric Substance Use Research, Behavioral Research Methods
Discipline: Psychology
Citations: 111
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Authors: K Vodrahalli, T Gerstenberg
Year: 2022
Published in: Advances in Neural ..., 2022 - proceedings.neurips.cc
Institution: Columbia University, Princeton University, Intel, Stanford University, Massachusetts Institute of Technology
Research Area: Human-AI Collaboration, Human Behavior Modeling, Decision Making
Discipline: Artificial Intelligence
Citations: 70
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Authors: A Bergman, A Chinco, SM Hartzmark
Year: 2020
Published in: Start-Up Guide and ..., 2020 - papers.ssrn.com
Institution: Yale School of Management, University of Miami School of Business, New York University, Columbia University
Research Area: Survey Methodology, Experimental Design, Social Science Research Methods
Discipline: Social Science Research Methods
Citations: 34
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Authors: B Cowgill, F Dell'Acqua, S Matz
Year: 2020
Published in: AEA Papers and Proceedings, 2020 - aeaweb.org
Institution: Columbia University, Harvard Business School
Research Area: Algorithmic Fairness in Management, Economics
Discipline: Economics
Citations: 34