When Agents Persuade: Rhetoric Generation and Mitigation in LLMs

Julia Jose; Ritik Roongta; Rachel Greenstadt

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arXiv · 2603.04636

When Agents Persuade: Rhetoric Generation and Mitigation in LLMs

Julia Jose·Ritik Roongta·Rachel Greenstadt

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Abstract

Despite their wide-ranging benefits, LLM-based agents deployed in open environments can be exploited to produce manipulative material. In this study, we task LLMs with propaganda objectives and analyze their outputs using two domain-specific models: one that classifies text as propaganda or non-propaganda, and another that detects rhetorical techniques of propaganda (e.g., loaded language, appeals to fear, flag-waving, name-calling). Our findings show that, when prompted, LLMs exhibit propagandistic behaviors and use a variety of rhetorical techniques in doing so. We also explore mitigation via Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and ORPO (Odds Ratio Preference Optimization). We find that fine-tuning significantly reduces their tendency to generate such content, with ORPO proving most effective.

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Julia Jose, Ritik Roongta, Rachel Greenstadt. 2026-03-04. When Agents Persuade: Rhetoric Generation and Mitigation in LLMs. https://arxiv.org/abs/2603.04636

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