Albanian Dataset Shifts Focus to LLM Safety in Low-Resource Languages

The world of Large Language Models (LLMs) has been buzzing with innovations and breakthroughs, but a significant blind spot has persisted. Low-resource languages, often overshadowed by their high-resource counterparts, have lagged behind in safety evaluations. Enter AlbanianLLMSafety, a pioneering dataset that promises to change this narrative.

AlbanianLLMSafety: A major shift?

AlbanianLLMSafety is the first publicly available safety evaluation dataset tailored for LLMs in Albanian. With around 7.5 million speakers spread across Albania, Kosovo, North Macedonia, and the diaspora, the language represents a significant, yet underserved, community in the AI landscape. This dataset comprises 2,951 prompts across 11 categories, addressing pressing safety concerns like self-harm, violence, racism, child exploitation, and radicalization. Each prompt comes with an Albanian version, an English translation, and a detailed category label. This is a comprehensive toolkit designed to fill a gaping hole in safety evaluation for low-resource languages.

Why Should We Care?

Why does this matter? The paper's key contribution is its focus on a language community that's often overlooked in tech development. As AI systems become increasingly pervasive, ensuring their safety and inclusiveness is essential. Ignoring low-resource languages means excluding significant portions of the global population from the benefits of these technologies. By providing a benchmark for Albanian, this dataset sets the stage for developing safer, more inclusive LLMs.

But let's not be naive. This dataset is a start, not a panacea. The safety categories addressed are vital, but are they exhaustive? What about other nuanced cultural factors that could be at play? The key finding here's the dataset's potential to prompt further exploration and refinement in AI safety measures.

Beyond Linguistic Boundaries

The implications of AlbanianLLMSafety extend beyond Albania. It's a call to action for other low-resource languages. How many more language communities are waiting for their safety datasets? This initiative should inspire similar efforts across the globe, highlighting the need to democratize AI technologies in a meaningful way.

Code and data are available upon request, positioning this as an artifact ready for further exploration. By supporting safety evaluation, fine-tuning, red-teaming, and guardrail development, AlbanianLLMSafety isn't just filling a gap, it's paving the way for a more inclusive AI future.

In the end, the question that remains is: who will be next to rise to the challenge? The stakes are high, and the opportunity to lead is ripe. It's time for the AI community to broaden its focus, ensuring that safety isn't a privilege of the few, but a right for all.