Researchers Introduce Schützen: Safety Evaluation Dataset for LLMs in German and Bulgarian
Researchers have introduced Schützen, a safety evaluation dataset designed to test large language model (LLM) behavior in Bulgarian and German contexts. The dataset addresses a significant gap in AI safety research, which has historically focused almost exclusively on English and Chinese. The findings show pronounced cross-language differences in how LLMs handle potentially harmful content, underscoring the need for language- and region-specific safety tools.
A team of researchers has released Schützen, a new benchmark dataset aimed at evaluating the safety of large language models (LLMs) in Bulgarian and German — covering both a low-resource and a high-resource language respectively. The work responds to a well-documented gap in AI safety evaluation: existing datasets are overwhelmingly English- and Chinese-centric, leaving many languages and their associated sociocultural, legal, and ethical contexts underrepresented. Experiments conducted with both multilingual and language-specific LLMs revealed significant differences in model safety behavior depending on the language used, meaning a model deemed safe in one language may not behave safely in another. The dataset is designed to assess 'answerability under risk,' probing whether models appropriately refuse or engage with potentially harmful, disrespectful, or biased prompts. The authors note that responsible deployment of LLMs in Germany and Bulgaria specifically requires tailored evaluation resources that reflect local norms and regulations. Both the dataset and accompanying code have been made publicly available to support further research.
What's missing
The study is limited to two languages, leaving open questions about generalizability to other underrepresented languages.
What different sources said
- arXiv cs.CLCenter
Sch\"utzen: Evaluating LLM Safety in Bulgarian and German Contexts
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