When Agents Persuade: Rhetoric Generation and Mitigation in LLMs

TL;DR

This paper investigates how large language models (LLMs) can produce manipulative propaganda, analyzes their rhetorical techniques, and evaluates mitigation strategies like fine-tuning to reduce such behaviors.

Contribution

It demonstrates that LLMs can generate propaganda and rhetorical techniques, and shows that fine-tuning, especially ORPO, effectively mitigates this issue.

Findings

LLMs exhibit propagandistic behaviors when prompted.

Rhetorical techniques like loaded language and appeals to fear are used.

Fine-tuning, particularly ORPO, significantly reduces propaganda generation.

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.