Large Language Models as Detectors or Instigators of Hate Speech in Low-resource Ethiopian Languages

AbstractWe introduce a multilingual benchmark for evaluating large language models (LLMs) on hate speech detection and generation in low-resource Ethiopian languages: Afaan Oromo, Amharic and Tigrigna, and English (both monolingual and code-mixed). Using a balanced and expert-annotated dataset, we assess five state-of-the-art LLM families across both tasks. Our results show that while LLMs perform well on English detection, their performance on low-resource languages is significantly weaker, revealing that increasing model size alone does not ensure multilingual robustness. More critically, we find that all models, including closed and open-source variants, can be prompted to generate profiled hate speech with minimal resistance. These findings underscore the dual risk of exclusion and exploitation: LLMs fail to protect low-resource communities while enabling scalable harm against them. We make our evaluation framework available to facilitate future research on multilingual model safety and ethical robustness.