When Agents Persuade: Rhetoric Generation and Mitigation in LLMs
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 Prefer...
| Authors | Julia Jose, Ritik Roongta, Rachel Greenstadt |
|---|---|
| Primary category | cs.AI |
| Published | 4 March 2026 |
| Last updated at source | 1 April 2026 |
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