Israel Abebe Azime, Jesujoba O. Alabi, Iffat Maab, Alexander D. Cai, Tadesse Destaw Belay, Atnafu Lambebo Tonja, Jonas Kgomo, Folasade Alabi · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26474 · open-access PDF
大型言語モデル(LLMs)は、ユーザーと相互作用するように設計され、有害または有毒なコンテンツに適切に対応するように訓練されていますが、さまざまな言語的および文化的文脈で適切な対応を確保することは、安全リスクが異なる形で表現または解釈される可能性があるため、安全なAIシステムを構築するには、安全リスクを検出し、その性質を理解し、適切に対応し、各段階に複雑さを加える言語的および文化的変異を考慮する必要があります。
Machine translation (Meta M2M100); the English original follows.
Large language models (LLMs) designed to interact with users are typically trained to respond appropriately to harmful or toxic content. However, ensuring appropriate responses becomes more challenging in diverse linguistic and cultural contexts, where safety risks may be expressed or interpreted differently. Building safe AI systems therefore requires detecting safety risks, understanding their nature, and responding appropriately, while accounting for linguistic and cultural variations that add complexity to each stage. In this work, we introduce AfriGuard, a culturally grounded multilingual
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