arXiv:2605.26954v1 Announce Type: new Abstract: 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 radicali
The rapid development and deployment of LLMs have highlighted the critical need for safety evaluations, which are now being extended to low-resource languages previously overlooked.
This development addresses the critical underserved area of AI safety in low-resource languages, crucial for ethical AI deployment and expanding AI's global utility beyond high-resource linguistic hegemony.
The availability of the first Albanian LLM safety dataset enables specific, culturally nuanced safety evaluations, leading to more robust and equitable AI model development for these linguistic communities.
- · Albanian-speaking communities
- · AI ethics researchers
- · Developers of multilingual LLMs
- · Governments focused on linguistic sovereignty
- · Monolingual LLM development teams
- · Users in low-resource languages relying on inadequately tested AI
AI models will begin to be evaluated for safety and bias in Albanian, potentially leading to safer and more relevant AI applications for Albanian speakers.
This initiative could spur similar efforts for other low-resource languages, fostering a more inclusive global AI ecosystem and increasing demand for diverse linguistic data.
Improved safety in LLMs for diverse languages could reduce 'AI colonialism' accusations and allow smaller nations to build more trusted domestic AI capabilities, potentially contributing to sovereign AI initiatives.
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