Incivility and Rigidity: Evaluating the Risks of Fine-Tuning LLMs for Political Argumentation

TL;DR

This paper investigates how fine-tuning large language models on different political discourse datasets influences their ability to generate civil, rhetorically sound, and deliberative arguments, highlighting challenges and evaluation methods.

Contribution

It introduces a rhetorical evaluation rubric and analyzes the effects of data composition and prompting on model output quality in political argumentation.

Findings

Reddit-finetuned models produce safer but more rigid arguments.

Cross-platform fine-tuning increases toxicity and adversarial tone.

Prompting reduces overt toxicity but doesn't eliminate data noise effects.

Abstract

Incivility on platforms such as Twitter (now X) and Reddit complicates the development of AI systems that can support productive, rhetorically sound political argumentation. We present experiments with \textit{GPT-3.5 Turbo} fine-tuned on two contrasting datasets of political discourse: high-incivility Twitter replies to U.S. Congress and low-incivility posts from Reddit's \textit{r/ChangeMyView}. Our evaluation examines how data composition and prompting strategies affect the rhetorical framing and deliberative quality of model-generated arguments. Results show that Reddit-finetuned models generate safer but rhetorically rigid arguments, while cross-platform fine-tuning amplifies adversarial tone and toxicity. Prompt-based steering reduces overt toxicity (e.g., personal attacks) but cannot fully offset the influence of noisy training data. We introduce a rhetorical evaluation rubric - covering justification, reciprocity, alignment, and authority - and provide implementation guidelines for authoring, moderation, and deliberation-support systems.