Explore 3 peer-reviewed studies by P R Ttger in Political Persuasion and Large Language Models (2023–2025). Discover research powered by Prolific's participant panel.
This page lists 3 peer-reviewed papers authored or co-authored by P R Ttger in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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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, Large Language Models
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
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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, Large Language Models, 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
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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
