Browse 4 peer-reviewed papers from Prolific spanning Data Quality, Human-centered AI evaluation (2021–2026). Research powered by Prolific's high-quality participant data.
This page lists 4 peer-reviewed papers from researchers at Prolific in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: N Petrova, A Gordon, E Blindow
Year: 2026
Published in: Open review
Institution: Prolific
Research Area: Human-centered AI evaluation, Bayesian statistics, Responsible AI, AI alignment, LLM Evaluation
Discipline: Machine Learning, Artificial Intelligence
The study introduces HUMAINE, a multidimensional evaluation framework for LLMs, revealing demographic-specific preference variations and ranking google/gemini-2.5-pro as the top-performing model with a posterior probability of 95.6%.
Methods: Multi-turn naturalistic conversations analyzed using a hierarchical Bayesian Bradley-Terry-Davidson model with post-stratification to census data, stratified across 22 demographic groups.
Key Findings: Performance of 28 LLMs across five human-centric dimensions, accounting for demographic-specific preferences.
Sample Size: 23404
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Authors: J Tomczak, A Gordon, J Adams, JS Pickering
Year: 2023
Published in: Frontiers in Human ..., 2023 - frontiersin.org
Institution: Prolific, University of Leeds, Gorilla
Research Area: Online Research Protocols, Human Neuroscience, Behavioral Research
Discipline: Human Neuroscience
Citations: 31
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Authors: E Peer, D Rothschild, A Gordon, E Damer
Year: 2022
Published in: Behavior Research Methods, 2022 - Springer
Institution: The Hebrew University of Jerusalem, Microsoft Research, Prolific
Research Area: Online Behavioral Research, Data Quality, Research Methods
Discipline: Computational Social Science, Behavioral Research
Citations: 2112
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Authors: L Litman, A Moss, C Rosenzweig
Year: 2021
Published in: Choosing the right ..., 2021 - papers.ssrn.com
Institution: Prolific
Research Area: Online Research Methods, Crowdsourcing Platforms, Data Quality, Participant Recruitment
Discipline: Computational Social Science, Behavioral Research Methods
Citations: 133