Benchmarking Offensive Language Detection in Persian and Pashto

AbstractOffensive language detection and target identification are essential for maintaining respectful online environments. While these tasks have been widely studied for English, comparatively less attention has been given to other language, including Persian and Pashto, and the effectiveness of recent large language models for these languages remains underexplored. To address this gap, we created a comprehensive benchmark of diverse modeling approaches in Persian and Pashto. Our evaluation covers zeroshot, fine-tuned, and cross-lingual transfer settings, analyzing when detection succeeds or fails across different model approaches. This study provides one of the first systematic analyses of offensive language detection and crosslingual transfer between these languages.