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LLM safety fails in African languages, study finds

A new study published on arXiv evaluated the safety of six large language models, including GPT-4o mini and Gemini 2.0 Flash, when tested with adversarial prompts in Yoruba and Nigerian Pidgin. The research found that English-centric safety alignments do not effectively transfer to these low-resource African languages, leading to increased attack success rates and a failure to understand prompts. GPT-4o mini showed a significant safety discrepancy, while Aya 101, despite its African language training, performed poorly in comprehension. The findings underscore the critical need for language-specific safety evaluations for LLMs used in diverse linguistic contexts. AI

IMPACT Highlights critical gaps in LLM safety for low-resource languages, necessitating language-specific evaluations and potentially impacting global AI deployment strategies.

RANK_REASON Academic paper detailing research findings on LLM safety in specific languages. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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LLM safety fails in African languages, study finds

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Academic paper detailing research findings on LLM safety in specific languages. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Paula Chikaodinaka Anyanwu, Valdini Douglace Lemofouet, Blessing Ngozi Uzor, Ifeoluwa Dorcas Awotunde, Danielle Blanche Kapsa, Tassallah Abdullahi, Clemencia Siro, Idris Abdulmumin, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad ·

    Red-Teaming Large Language Models in Low-Resource African Languages: A Case Study of Yoruba and Nigerian Pidgin

    arXiv:2610.39590v1 Announce Type: cross Abstract: As large language models (LLMs) become more widespread in Africa, their safety in low-resource languages remains largely untested since most red-teaming work focuses on English. This study evaluates six state-of-the-art LLMs using…