• argument convincingness testing
• automatic counterspeech evaluation
• computational persuasion
• large language models

The growing ability of Large Language Models (LLMs) to generate persuasive content has exposed an "evaluation crisis," as methodologies to measure argumentative efficacy are unreliable or unscalable. This thesis introduces LLM-IMPACT, a novel framework that evaluates arguments via simulated, multi-turn debates. Grounded in a unique dataset from the r/ChangeMyView subreddit, the framework's effectiveness was rigorously tested. Our best configuration, a zero-shot Qwen 3 (14B) model guided by an empirically optimized prompt, demonstrated significant reasoning capabilities by achieving a median F-beta macro score of 66.73% and outperforming a majority-class dummy baseline by over 17 percentage points.

The final evaluation on a test set of unseen data confirmed the excellent generalization capabilities of our approach. The top-performing zero-shot model maintained its high performance with remarkable consistency. Furthermore, even the sub-optimal configurations derived from fine-tuning demonstrated highly stable and predictable behavior, successfully generalizing their distinct performance profiles to the test set. This robust generalization across different model configurations validates the methodological soundness of the LLM-IMPACT framework as a reliable tool for persuasion analysis. While fine-tuning did not surpass the zero-shot baseline, our results definitively show that sophisticated prompt engineering can produce stable and highly generalizable models.