arXiv:2606.11316v1 Announce Type: new Abstract: Large language models are increasingly deployed across professional domains, bringing hard-to-predict risks, including the generation of harmful or disrespectful content. Although substantial progress has been made in developing safety evaluation datasets, existing resources remain overwhelmingly English- and Chinese-centric. This limitation is particularly pronounced when evaluating languages that operate within shared sociocultural, legal, and ethical contexts. To address this gap, we introduce Sch\"{u}tzen: a German--Bulgarian safety dataset d
As LLMs are increasingly deployed across diverse contexts, the immediate need to address their safety in non-English, culturally specific settings becomes critical.
This development highlights the growing recognition that AI safety and ethics must be globally inclusive, moving beyond a narrow linguistic and cultural focus.
The availability of datasets like Sch"utzen will enable more robust and culturally nuanced safety evaluations for LLMs beyond English and Chinese, addressing specific geopolitical and social contexts.
- · European AI developers
- · Multilingual AI research
- · Ethical AI advocates
- · German and Bulgarian language users
- · Developers relying solely on English-centric safety benchmarks
- · Homogenous AI safety evaluation approaches
It enables better identification and mitigation of harmful content generated by LLMs in specific European languages and cultural contexts.
This supports the development of more trustworthy and regionally compliant AI systems, potentially accelerating AI adoption in sensitive sectors within these countries.
It could lead to a broader trend of nation-specific or regional AI safety standards and dataset development, diversifying global AI governance frameworks.
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Read at arXiv cs.CL