University Of London: Research Institution — Prolific Citations Library

Browse 24 peer-reviewed papers from University Of London spanning LLM, Crowdsourcing (2022–2025). Research powered by Prolific's high-quality participant data.

This page lists 24 peer-reviewed papers from researchers at University Of London in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 24)

Large Language Models Are More Persuasive Than Incentivized Human Persuaders

Authors: P. Schoenegger, F. Salvi, J. Liu, X. Nan, R. Debnath, B. Fasolo, E. Leivada, G. Recchia, F. Günther, A. Zarifhonarvar, J. Kwon, Z. Ul Islam, M. Dehnert, D. Y. H. Lee, M. G. Reinecke, D. G. Kamper, M. Kobaş, A. Sandford, J. Kgomo, L. Hewitt, S. Kapoor, K. Oktar, E. E. Kucuk, B. Feng, C. R. Jones, I. Gainsburg, S. Olschewski, N. Heinzelmann, F. Cruz, B. M. Tappin, T. Ma, P. S. Park, R. Onyonka, A. Hjorth, P. Slattery, Q. Zeng, L. Finke, I. Grossmann, A. Salatiello, E. Karger

Year: 2025

Published in: arXiv preprint arXiv ..., 2025 - arxiv.org

Institution: London School of Economics and Political Science, University of Cambridge, University College London, Massachusetts Institute of Technology, University of Oxford, Modulo Research, Stanford University, Federal Reserve Bank of Chicago, ETH Zürich, University of Johannesburg

Research Area: Computation and Language

Discipline: Social Science, Artificial Intelligence

This paper compares a frontier LLM (Claude Sonnet 3.5) against incentivized human persuaders in a conversational quiz setting, finding that the AI's persuasion capabilities surpass those of humans with real-money bonuses tied to performance.

Citations: 16
The levers of political persuasion with conversational artificial intelligence

Authors: K Hackenburg, BM Tappin, L Hewitt, E Saunders

Year: 2025

Published in: Science, 2025 - science.org

Institution: London School of Economics and Political Science, Stony Brook University

Research Area: Political Persuasion with Conversational AI, LLM, Factual Accuracy in AI Systems.

Discipline: Political Science, Computational Social Science

This Science paper shows that conversational AI chatbots can systematically influence political opinions at scale, and that techniques like post-training and prompting make them far more persuasive—but that increased persuasion is tied to reduced factual accuracy in what the AI says.

Citations: 12
Crowdsourced comparative judgement for evaluating learner texts: How reliable are judges recruited from an online crowdsourcing platform?

Authors: P Thwaites, N Vandeweerd, M Paquot

Year: 2025

Published in: Applied Linguistics, 2025 - academic.oup.com

Institution: University College Londonouvain, Radboud University Nijmegen, Fonds de la Recherche Scientifique – FNRS

Research Area: Applied Linguistics, Educational Assessment, Crowdsourcing

Discipline: Applied Linguistics

The study demonstrates that crowdsourcing platforms can recruit judges to evaluate learner texts with reliability and validity comparable to assessments conducted by trained linguists.

Methods: Judges recruited via an online crowdsourcing platform conducted comparative judgement assessments of learner texts to measure writing proficiency.

Key Findings: Reliability and concurrent validity of learner text evaluations performed via crowdsourced judges compared to linguist evaluations.

Citations: 10
Leveraging Social Media and Crowdsourcing to Recruit and Retain Military Veterans With Posttraumatic Stress Disorder or Experience of Harmful Gambling ...

Authors: C Heath, JM Williams, D Leightley

Year: 2025

Published in: JMIR mHealth and ..., 2025 - mhealth.jmir.org

Institution: Swansea University, King's College London, Reykjavík University

Research Area: mHealth Interventions, Crowdsourcing, Social Media Recruitment, Mental Health Research (PTSD, Harmful Gambling)

Discipline: Digital Health, Mental Health Research

Social media and online platforms like Facebook and Prolific were effective but faced challenges in recruiting and retaining military veterans with PTSD or harmful gambling for a digital mHealth intervention pilot study.

Methods: Multiple recruitment strategies were used, including paid and unpaid advertisements on Facebook, Prolific, direct mailing, event hosting with veterans' charities, snowball sampling, and incentives.

Key Findings: The effectiveness of different recruitment strategies for enrolling military veterans with PTSD or harmful gambling into a digital intervention study.

Sample Size: 79
The PRISM alignment dataset: What participatory, representative and individualised human feedback reveals about the subjective and multicultural alignment of large ...

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
Organic or diffused: Can we distinguish human art from ai-generated images?

Authors: AYJ Ha, J Passananti, R Bhaskar, S Shan

Year: 2024

Published in: Proceedings of the ..., 2024 - dl.acm.org

Institution: University of California Santa Barbara, The University of Chicago, Institute of Education, University College London

Research Area: Human-Computer Interaction (HCI), Generative AI, Digital Forensics

Discipline: Human-Computer Interaction (HCI), Generative AI, Digital Forensics

The paper investigates the effectiveness of different approaches, including both human and automated detectors, in distinguishing human art from AI-generated images, finding that a combination of methods offers the best performance despite persistent weaknesses.

Methods: Comparison of human art across 7 styles with AI-generated images from 5 generative models, assessed using 5 automated detectors and 3 human groups (crowdworkers, professional artists, expert artists).

Key Findings: Detection accuracy and robustness of human and automated methods in identifying AI-generated images under benign and adversarial conditions.

DOI: 10.1145/3658644.3670306

Citations: 52

Sample Size: 3993
The impact of the cost of living crisis and food insecurity on food purchasing behaviours and food preparation practices in people living with obesity

Authors: RA Stone, A Brown, F Douglas, M Green, E Hunter, M Lonnie, AM Johnstone, CA Hardman, FIO-Food Team

Year: 2024

Published in: Science Direct

Institution: Robert Gordon University, University College London, University of Aberdeen, University of Liverpool

Research Area: Food Insecurity, Public Health, Behavioral Economics (focusing on food purchasing behaviors and preparation practices related to obesity, cost of living)

Discipline: Public Health

The study examines how the UK cost of living crisis affects food purchasing and preparation behaviors in people with obesity, highlighting food insecurity and associated coping strategies, and calls for policy interventions to improve access to healthy foods.

Methods: An online survey was conducted with self-reported data on food insecurity, diet quality, cost of living impact, and food purchasing/preparation behaviors among adults with BMI ≥30 kg/m2 in England or Scotland.

Key Findings: Food insecurity, diet quality, impacts of the cost of living crisis, food purchasing behaviors, and food preparation practices among participants.

Citations: 46

Sample Size: 583
Evidence of a log scaling law for political persuasion with large language models

Authors: K Hackenburg, BM Tappin, P Röttger, S Hale

Year: 2024

Published in: arXiv preprint arXiv ..., 2024 - arxiv.org

Institution: University of Oxford, The Alan Turing Institute, Royal Holloway, University of London, Bocconi University, Meedan

Research Area: LLM scaling laws, Political Persuasion, LLM, AI Social Science

Discipline: Political Science, Artificial Intelligence

Persuasiveness of messages generated by large language models follows a log scaling law with diminishing returns as model size increases, and task completion appears to primarily drive this capability.

Methods: Generated 720 persuasive messages on 10 U.S. political issues using 24 language models of varying sizes; evaluated persuasiveness through a large-scale randomized survey experiment.

Key Findings: Persuasiveness of large language model-generated political messages across different model sizes.

Citations: 17

Sample Size: 25982
Crowdsourcing samples for research on violent extremism: A research note

Authors: C Clemmow, I van der Vegt, B Rottweiler

Year: 2024

Published in: ... and political violence, 2024 - Taylor & Francis

Institution: University College London

Research Area: Crowdsourcing for Violent Extremism Research

Discipline: Computational Social Science

Citations: 12
Large language models amplify human biases in moral decision-making

Authors: V Cheung, M Maier, F Lieder

Year: 2024

Published in: Psyarxiv preprint, 2024 - files.osf.io

Institution: University College LondonA

Research Area: AI Ethics, Moral Decision-Making, Cognitive Biases in LLMs, AI Bias

Discipline: Artificial Intelligence, Ethics

Citations: 11
AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy

Authors: P Schoenegger, P Park, E Karger, P Tetlock

Year: 2024

Published in: ArXiv

Institution: Federal Reserve Bank of Chicago, London School of Economics and Political Science, Massachusetts Institute of Technology, University of Pennsylvania

Research Area: LLM Assistants, Human Forecasting, Predictive Modeling, AI-Augmented Decision Making, LLM

Discipline: Artificial Intelligence, Behavioral Science
How human-AI feedback loops alter human perceptual, emotional and social judgements

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
Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Authors: HR Kirk, B Vidgen, P Röttger, SA Hale

Year: 2023

Published in: arXiv preprint arXiv:2303.05453, 2023 - arxiv.org

Institution: The Alan Turing Institute, University of Oxford, Imperial College London, King's College London, Google DeepMind

Research Area: Large Language Model Alignment, Safety, Personalization Risks

Discipline: Artificial Intelligence

DOI: https://doi.org/10.48550/arXiv.2303.05453

Citations: 146
Artificial intelligence can persuade humans on political issues

Authors: H Bai, J Voelkel, J Eichstaedt, R Willer

Year: 2023

Published in: 2023 - researchsquare.com

Institution: Stanford University, London Business School, Dartmouth College, Stanford Graduate School of Business

Research Area: Political Persuasion, Social Influence of AI, Cognitive Science

Discipline: Political Science, Social Science

Citations: 100
Human feedback is not gold standard

Authors: T Hosking, P Blunsom, M Bartolo

Year: 2023

Published in: arXiv preprint arXiv:2309.16349, 2023 - arxiv.org

Institution: Cohere, University of Edinburgh, University College London

Research Area: LLM Evaluation, Limitations of Human Preference Scores, Human-Computer Interaction (HCI) in AI Training

Discipline: Artificial Intelligence

DOI: https://doi.org/10.48550/arXiv.2309.16349

Citations: 72
Susceptibility to influence of large language models

Authors: LD Griffin, B Kleinberg, M Mozes, KT Mai, M Vau

Year: 2023

Published in: arXiv preprint arXiv ..., 2023 - arxiv.org

Institution: University College London, Tilburg University

Research Area: LLM Influence, Psychology, Mental Health Research, LLM

Discipline: Artificial Intelligence, Psychology

Citations: 30
Large language models respond to influence like humans

Authors: L Griffin, B Kleinberg, M Mozes, K Mai

Year: 2023

Published in: Proceedings of the ..., 2023 - aclanthology.org

Institution: University College London, Tilburg University

Research Area: LLM Influence and Persuasion, LLM

Discipline: Social Science

Citations: 25
Multimodality and attention increase alignment in natural language prediction between humans and computational models

Authors: V Kewenig, A Lampinen, SA Nastase

Year: 2023

Published in: arXiv preprint arXiv ..., 2023 - arxiv.org

Institution: University College London, Princeton University, Exeter University

Research Area: Computational Linguistics, Cognitive Science

Discipline: Computational Linguistics

DOI: https://doi.org/10.48550/arXiv.2308.06035

Citations: 3
On the realness of people who do not exist: The social processing of artificial faces

Authors: R Tucciarelli, N Vehar, S Chandaria, M Tsakiris

Year: 2022

Published in: Iscience, 2022 - cell.com

Institution: University of London Royal Holloway

Research Area: Social Processing, Artificial Faces, Face Perception

Discipline: Psychology

Citations: 53
Large language models are not zero-shot communicators

Authors: LE Ruis, A Khan, S Biderman, S Hooker, T Rocktäschel

Year: 2022

Published in: 2022 - openreview.net

Institution: MILA, University of Toronto, Stanford University, Hugging Face, Imperial College London

Research Area: Natural Language Processing, LLM, Communication

Discipline: Natural Language Processing

Citations: 52