We present a benchmark dataset for evaluating language model safety in Polish, addressing the underrepresentation of medium-resource languages in existing safety assessments. Our dataset includes both original and adversarially perturbed examples. We fine-tune and evaluate multiple models—LlamaGuard-3-8B, a HerBERT-based classifier, and PLLuM—and find that the HerBERT-based model outperforms others, especially under adversarial conditions.

In this study, we explore the use of Large Language Models (LLMs) to counteract hate speech. We conducted the first real-life A/B test assessing the effectiveness of LLM-generated counter-speech. During the experiment, we posted 753 automatically generated responses aimed at reducing user engagement under tweets that contained hate speech toward Ukrainian refugees in Poland.Our work shows that interventions with LLM-generated responses significantly decrease user engagement, particularly for original tweets with at least ten views, reducing it by over 20%. This paper outlines the design of our automatic moderation system, proposes a simple metric for measuring user engagement and details the methodology of conducting such an experiment. We discuss the ethical considerations and challenges in deploying generative AI for discourse moderation.