Browse 7 peer-reviewed papers from Columbia University spanning LLM Limitations, Social Media (2020–2025). Research powered by Prolific's high-quality participant data.
This page lists 7 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: 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: 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
