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University Of California Irvine Papers

0 research papers from University Of California Irvine that used Prolific for participant recruitment.

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University Of California Irvine

University Of California Irvine: Research Institution — Prolific Citations Library

Browse 3 peer-reviewed papers from University Of California Irvine spanning Computational Social Science, Computational Linguistics (2023–2025). Research powered by Prolific's high-quality participant data.

This page lists 3 peer-reviewed papers from researchers at University Of California Irvine in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (3 of 3)

What large language models know and what people think they know

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
Impact of annotator demographics on sentiment dataset labeling

Authors: Y Ding, J You, TK Machulla, J Jacobs, P Sen

Year: 2025

Published in: Proceedings of the ..., 2022 - dl.acm.org

Institution: University of California Irvine, University of Florida, State University of New York at Buffalo, University of Waterloo, Virginia Tech

Research Area: Computational Social Science, Human-Computer Interaction, Sentiment Analysis

Discipline: Computational Social Science

Demographic differences among annotators significantly affect sentiment dataset labels, causing up to a 4.5% accuracy difference in sentiment prediction models.

Methods: Crowdsourced annotations from >1000 workers combined with demographic data; analysis of multimodal sentiment datasets and evaluation using machine learning models.

Key Findings: Impact of annotator demographics on sentiment labeling and its effect on model predictions.

DOI: https://doi.org/10.1145/3555632

Citations: 28

Sample Size: 1000
Influencing human-AI interaction by priming beliefs about AI can increase perceived trustworthiness, empathy and effectiveness

Authors: P Pataranutaporn, R Liu, E Finn, P Maes

Year: 2023

Published in: Nature Machine Intelligence, 2023 - nature.com

Institution: University of California Irvine

Research Area: Human-AI Interaction, Behavioral Science

Discipline: Human-Computer Interaction, Artificial Intelligence

DOI: https://doi.org/10.1038/s42256-023-00720-7

Citations: 180