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AfriGuard: Safety and Robust Refusal For African Languages

Israel Abebe Azime, Jesujoba Oluwadara Alabi, Iffat Maab, Alexander D. Cai, Tadesse Destaw Belay, Atnafu Lambebo Tonja, Jonas Kgomo, Folasade Alabi, Salomey Osei, Saminu Muhammad Aliyu, Nkechinyere Faith Aguobi, Bontu Fufa Balcha, Blessing Sibanda, David S. Davis, Mouhamadane Mboup, Daud Olamide Abolade, Neo Putini, Louis Powell, Kiant'e Brantley, Philipp Slusallek · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26474 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

旨在与用户互动的大型语言模型(LLMs)通常被训练以适当地应对有害或有毒的内容。然而,确保适当的响应在各种语言和文化背景下变得更加具有挑战性,安全风险可以以不同的方式表达或解释。因此,构建安全的AI系统需要在10种非洲语言和英语中检测安全风险,理解其性质,并做出适当的响应,同时考虑到在每个阶段增加复杂性的语言和文化变异。 在这项工作中,我们引入了AfriGuard,一种以文化为基础的多语言数据集,用于研究有害的意图,安全类别的理解和拒绝行为。我们的发现表明,防护车模型在安全意图和安全类别上的开放源代码和文化变异中表现不佳,而防护车模型

Machine translation (Meta M2M100); the English original follows.

Abstract

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 dataset for studying harmful intent, safety-category understanding, and refusal behavior across 10 African languages and English. Our findings reveal that guardrail models underperform both open- and closed-source LLMs on safety-intent and safety-category classification, while models show limited understanding of appropriate safety behavior, scoring below 60% F1. We further find that models often fail to respond appropriately to unsafe prompts not because they explicitly refuse, but because they produce linguistically inadequate responses that result in incidental non-assistance rather than proper refusals. Finally, using AfriGuard, we develop efficient guardrail models that achieve performance competitive with models up to 30x larger, introduce refusal detectors that improve average performance by 43.9% over existing detectors, and demonstrate that supervised fine-tuning can substantially improve overall safety performance.

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Topics: Computer science, Cultural diversity, Risk analysis (engineering), Psychology, Human factors and ergonomics, Risk assessment

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