Multilingual LLMs are increasingly used as context-aware judges in real-world information systems under the assumption that equivalent content receives equivalent judgments across languages. We examine this assumption through brand safety, a global application where automated ratings can affect advertisers’ reputations, publishers’ revenues, and users’ access to news. We construct a benchmark of LLM-generated safety ratings for 10,467 semantically aligned news articles across 13 languages. We find systematic cross-lingual disagreement appearing in more than 96% of cases where at least one language receives a non-zero risk rating. Suitability ratings differ significantly by language, controlling for run, category, and article. In the main model, English, German, and French content is generally rated more strictly, while Polish, Hungarian, Greek, Turkish, and Persian content is rated more leniently. Robustness checks with two additional LLMs show that significant language effects persist, though directional patterns vary by model. These findings show that multilingual LLM safety judgments can produce unequal outcomes for semantically equivalent content.

Unsupervised automatic readability assessment (ARA) methods have important practical and research applications (e.g., ensuring medical or educational materials are suitable for their target audiences). In this paper, we propose a new zero-shot prompting methodology for ARA and present the first comprehensive evaluation of using large language models (LLMs) as an unsupervised ARA method by testing 10 diverse open-source LLMs (e.g., different sizes and developers) on 14 diverse datasets (e.g., different text lengths and languages). Our findings show that our proposed prompting methodology outperforms prior methods on 13 of the 14 datasets. Furthermore, we propose LAURAE, which combines LLM and readability formula scores to improve robustness by capturing both contextual and shallow (e.g., sentence length) features of readability. Our evaluation demonstrates that LAURAE robustly outperforms prior methods across languages, text lengths, and amounts of technical language.