University Of Oxford: Research Institution — Prolific Citations Library

Browse 21 peer-reviewed papers from University Of Oxford spanning LLM, Political Persuasion (2021–2025). Research powered by Prolific's high-quality participant data.

This page lists 21 peer-reviewed papers from researchers at University Of Oxford in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 21)

Comparing the persuasiveness of role-playing large language models and human experts on polarized US political issues

Authors: K Hackenburg, L Ibrahim, BM Tappin, M Tsakiris

Year: 2025

Published in: AI & SOCIETY, 2025 - Springer

Institution: Oxford Internet Institute, University of Oxford

Research Area: Political Communication and Persuasion, LLM

Discipline: Political Science, Artificial Intelligence

GPT-4's ability to generate persuasive messages rivaled human experts on polarized US political issues, suggesting AI tools may have significant implications for political campaigns and democracy.

Methods: Pre-registered experiment where GPT-4 generated partisan role-playing persuasive messages, which were compared to those from human persuasion experts.

Key Findings: Persuasive impact of GPT-4-generated messages versus human expert messages on U.S. political issues.

Citations: 35

Sample Size: 4955
Scaling language model size yields diminishing returns for single-message political persuasion

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

Year: 2025

Published in: Proceedings of the ..., 2025 - pnas.org

Institution: University of California Berkeley, University of Cambridge, University of Oxford, Max Planck Institute

Research Area: Political Persuasion, LLM

Discipline: Computational Social Science, Political Science

Scaling language model sizes leads to diminishing returns in generating persuasive political messages, with larger models providing minimal gains compared to smaller ones after controlling for task completion metrics like coherence and relevance.

Methods: Generated 720 political messages using 24 LLMs of varying sizes and tested their persuasiveness through a large-scale randomized survey experiment.

Key Findings: Persuasive capability of language models across different sizes in generating political messages.

Citations: 31

Sample Size: 25982
Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models

Authors: L Ibrahim, C Akbulut, R Elasmar, C Rastogi, M Kahng, MR Morris, KR McKee, V Rieser, M Shanahan, L Weidinger

Year: 2025

Published in: arXiv preprint arXiv:2502.07077, 2025•arxiv.org

Institution: Google DeepMind, Google, University of Oxford

Research Area: Multimodal conversational AI, conversational AI, Evaluation methodology, benchmarking

Discipline: Computer Science, Natural Language Processing (NLP), Human–Computer Interaction (HCI)

The paper evaluates anthropomorphic behaviors in SOTA LLMs through a multi-turn methodology, showing that such behaviors, including empathy and relationship-building, predominantly emerge after multiple interactions and influence user perceptions.

Methods: Multi-turn evaluation of 14 anthropomorphic behaviors using simulations of user interactions, validated by a large-scale human subject study.

Key Findings: Anthropomorphic behaviors in large language models, including relationship-building and pronoun usage, and their perception by users.

Citations: 26

Sample Size: 1101
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
Age and gender distortion in online media and large language models

Authors: D Guilbeault, S Delecourt, BS Desikan

Year: 2025

Published in: Nature, 2025 - nature.com

Institution: Stanford University, University of California Berkeley, University of Oxford

Research Area: AI Bias, Media Representation, Social Science

Discipline: Computational Social Science, Artificial Intelligence

The study highlights age-related gender bias in online media and language models, showing women are portrayed as younger than men, especially in high-status occupations, and explores how algorithms amplify these biases.

Methods: Analysis of 1.4 million images and videos from online sources and nine language models, followed by a pre-registered experiment involving participants to evaluate biases in internet content and algorithms.

Key Findings: Age and gender bias in occupational depiction across online platforms and language models, as well as its influence on beliefs and hiring preferences.

Citations: 4

Sample Size: 459
Multimodal large language models can make context-sensitive hate speech evaluations aligned with human judgement

Authors: T Davidson

Year: 2025

Published in: Nature Human Behaviour, 2025 - nature.com

Institution: University of Oxford, Davidson College

Research Area: Hate Speech Evaluation, Multimodal LLMs, Social Bias, Computational Law, AI Bias, AI Evaluation

Discipline: Artificial Intelligence

The study demonstrates that larger multimodal large language models (MLLMs) can align closely with human judgement in context-sensitive hate speech evaluations, though they still exhibit biases and limitations.

Methods: Conjoint experiments where simulated social media posts varying in attributes like slur usage and user demographics were evaluated by MLLMs and compared to human judgements.

Key Findings: The capacity of MLLMs to evaluate hate speech in a context-sensitive manner and their alignment with human judgement, while assessing biases and responsiveness to contextual cues.

Sample Size: 1854
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
Why human-AI relationships need socioaffective alignment

Authors: HR Kirk, I Gabriel, C Summerfield, B Vidgen

Year: 2024

Published in: Humanities and Social ..., 2025 - nature.com

Institution: Oxford Internet Institute, University of Oxford

Research Area: Socioaffective Alignment in Human-AI Relationships, AI Ethics, Behavioral Science

Discipline: Artificial Intelligence, Behavioral Science

The paper emphasizes the need for socioaffective alignment in human-AI relationships to ensure AI systems support human psychological needs rather than exploit them, as interactions with AI transition from transactional to sustained engagement.

Methods: Conceptual analysis of socioaffective dynamics in human-AI interactions, framed through psychological theories and principles.

Key Findings: Exploration of how AI systems impact socioaffective relationships, psychological needs, autonomy, companionship, and human well-being.

DOI: https://doi.org/10.1057/s41599-025-04532-5

Citations: 59
First-person fairness in chatbots

Authors: T Eloundou, A Beutel, DG Robinson

Year: 2024

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

Institution: OpenAI, Google DeepMind, Google, University of Oxford

Research Area: Fairness in LLM, AI Bias, AI Ethics

Discipline: Artificial Intelligence, Social Science

The paper introduces a counterfactual approach to evaluate 'first-person fairness' in chatbots, demonstrating that reinforcement learning can mitigate biases based on demographics across extensive chatbot interactions.

Methods: The study uses a Language Model as a Research Assistant (LMRA) to quantitatively and qualitatively assess biases based on demographics across millions of chatbot interactions, covering 66 tasks in 9 domains and involving two genders and four races. Bias evaluations are corroborated by independent...

Key Findings: Demographic biases in chatbot responses, including harmful stereotypes and response differences by gender and race, across diverse tasks and domains.

DOI: https://doi.org/10.48550/arXiv.2410.19803

Citations: 33

Sample Size: 6000000
The ethical, societal, and global implications of crowdsourcing research

Authors: S Du, MT Babalola, P D'cruz, E Dóci

Year: 2024

Published in: Journal of Business ..., 2024 - Springer

Institution: Nottingham University Business School, University of Reading, Oxford Brookes University, University of Portsmouth

Research Area: Crowdsourcing Ethics, Social Sciences, Organizational Behavior

Discipline: Social Science

The paper explores the ethical, societal, and global implications of using crowdsourcing platforms for research, emphasizing the need for fair compensation, transparency, and consideration of global disparities between the Global North and South.

Methods: The paper provides a conceptual analysis and critique of crowdsourcing research practices, focusing on ethical and societal considerations.

Key Findings: Ethical, societal, and global implications of crowdsourcing research practices, including data quality, reporting transparency, fair remuneration, and the role of global disparities.

Citations: 24
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
Whose preferences? differences in fairness preferences and their impact on the fairness of AI utilizing human feedback

Authors: M Lerner, F Dorner, E Ash, N Goel

Year: 2024

Published in: ... of the 62nd Annual Meeting of ..., 2024 - aclanthology.org

Institution: ETH Zürich, University of Oxford

Research Area: Fairness in AI, Content Moderation, Human-AI Alignment

Discipline: Computational Social Science

Citations: 5
Occupational prestige and occupational social value in the United Kingdom: New indices for the modern British economy

Authors: G Newlands, C Lutz

Year: 2024

Published in: Science Direct

Institution: BI Norwegian Business School, University of Oxford

Research Area: Occupational Sociology, Labor Economics

Discipline: Sociology
The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub

Authors: HR Kirk, C Osborne

Year: 2024

Published in: ArXiv

Institution: Alan Turing Institute, Oxford Internet Institute, Oxford University

Research Area: Computational Social Science, AI Community Analysis, Hugging Face Hub Activity

Discipline: Computational Social Science
Why women cheat: testing evolutionary hypotheses for female infidelity in a multinational sample

Authors: Macken Murphy, Caroline A. Phillips, Khandis R. Blake

Year: 2024

Published in: Science Direct

Institution: University of Melbourne, University of Oxford

Research Area: Evolutionary Psychology, Human Sexuality, Infidelity Studies

Discipline: Psychology
Evaluating the persuasive influence of political microtargeting with large language models

Authors: K Hackenburg, H Margetts

Year: 2023

Published in: Proceedings of the National Academy of ..., 2024 - pnas.org

Institution: Oxford University, Alan Turing Institute

Research Area: Political Persuasion, LLM, Political Science

Discipline: Political Science

DOI: https://doi.org/10.1073/pnas.2403116121

Citations: 153
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
The taste of colours

Authors: C Velasco, F Barbosa Escobar, C Spence, JS Olier

Year: 2023

Published in: Science Direct

Institution: Aarhus University, BI Norwegian Business School, Tilburg University, University of Copenhagen, University of Oxford

Research Area: Food Science

Discipline: Experimental Psychology, Food Science

Citations: 29
A framework for rigorous evaluation of human performance in human and machine learning comparison studies

Authors: HP Cowley, M Natter, K Gray-Roncal, RE Rhodes

Year: 2022

Published in: Scientific Reports, 2022 - nature.com

Institution: Johns Hopkins University Applied Physics Laboratory, University of Oxford, Johns Hopkins University, Johns Hopkins University Applied Physics Laboratory, University of Oxford, Johns Hopkins University

Research Area: Human-AI Interaction, Machine Learning Evaluation, AI Evaluation

Discipline: Human-Computer Interaction (HCI)

DOI: https://doi.org/10.1038/s41598-022-08078-3

Citations: 34
The experimenters' dilemma: inferential preferences over populations

Authors: N Gupta, L Rigotti, A Wilson

Year: 2021

Published in: arXiv preprint arXiv:2107.05064, 2021 - arxiv.org

Institution: University of Cambridge, University of Verona, University of Oxford, University of Pittsburgh

Research Area: Experimental Design, Research Methodology, Inferential Statistics

Discipline: Social Science Research Methods

Citations: 104