Safety evaluation of Large Language Models (LLMs) has largely focused on high-resource languages, leaving low-resource languages critically underserved. We present AlbanianLLMSafety, the first publicly available safety evaluation dataset for LLMs in Albanian, a linguistically distinct low-resource language with approximately 7.5 million speakers across Albania, Kosovo, North Macedonia, and the diaspora. The dataset contains 2,951 prompts spanning 11 safety categories, including self-harm, violence, racist content, child exploitation, and radicalization, with an average of 268 prompts per category. Each prompt is provided in Albanian with an English reference translation and a detailed category label. This resource addresses a significant gap in safety evaluation infrastruc-ture for low-resource languages and provides an essential benchmark for developing safer, more inclusive LLMs. The dataset will be provided upon request to support safety evaluation, fine-tuning, red-teaming, and guardrail development for Albanian-speaking communities.
@article{arxiv.2605.26954,
title = {AlbanianLLMSafety: A Safety Evaluation Dataset for Large Language Models in Albanian},
author = {Wajdi Zaghouani and Kholoud K. Aldous and Isra Fejzullaj},
journal= {arXiv preprint arXiv:2605.26954},
year = {2026}
}
Comments
Accepted at SIGUL2026 Workshop co-located with LREC2026