Llm Persuasion: Research Area — Prolific Citations Library

Discover 4 peer-reviewed studies in Llm Persuasion (2023–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 4 peer-reviewed papers in the research area of Llm Persuasion in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (4 of 4)

Ethics and Persuasion in Reinforcement Learning from Human Feedback: A Procedural Rhetorical Approach

Authors: S Lodoen, A Orchard

Year: 2025

Published in: arXiv preprint arXiv:2505.09576, 2025 - arxiv.org

Institution: Embry–Riddle Aeronautical University, University of Waterloo

Research Area: Reinforcement Learning from Human Feedback (RLHF), Procedural Rhetoric, LLM Persuasion, Ethics

Discipline: Artificial Intelligence, AI Ethics, Social Science

The paper uses procedural rhetoric to analyze how RLHF reshapes ethical, social, and rhetorical dimensions of generative AI interactions, raising concerns about biases, hegemonic language, and human relationships.

Methods: The study conducts a theoretical and rhetorical analysis based on Ian Bogost's concept of procedural rhetoric, examining how RLHF mechanisms influence language conventions, information practices, and social expectations.

Key Findings: Ethical and rhetorical implications of RLHF-enhanced LLMs on language usage, information seeking, and interpersonal dynamics.

DOI: https://doi.org/10.48550/arXiv.2505.09576

Citations: 3
A meta-analysis of the persuasive power of large language models

Authors: L Hölbling, S Maier, S Feuerriegel

Year: 2025

Published in: Scientific Reports, 2025 - nature.com

Institution: University of Lausanne, University of Zurich, University of St. Gallen

Research Area: LLMs in Persuasion, Meta-Analysis, Artificial Intelligence, Human-Computer Interaction (HCI)

Discipline: Artificial Intelligence

Large language models (LLMs) demonstrate similar persuasive performance to humans overall, but their effectiveness varies widely based on contextual factors such as model type, conversation design, and domain.

Methods: Systematic review and meta-analysis using Hedges' g to compute standardized effect sizes, with exploratory moderator analyses and publication bias checks (Egger's test, trim-and-fill analysis).

Key Findings: The persuasive effectiveness of LLMs compared to humans across various contexts and studies.

Sample Size: 17422
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
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