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LLM Safety Fails to Transfer Across Low-Resource Languages, Study Finds

By PulseAugur Editorial · [2 sources] ·

A new research paper published on arXiv highlights significant limitations in the cross-lingual safety of large language models (LLMs). The study focused on four African languages—Twi, Hausa, Amharic, and Swahili—and found that safety alignment developed in English does not effectively transfer to these low-resource languages. Using a novel dataset called LoDNA and a latent geometric framework, researchers discovered that harmful prompts retain less than 10% of the English refusal signal, indicating that current multilingual safety measures are superficial. AI

IMPACT Highlights critical gaps in LLM safety for non-English languages, potentially impacting global AI deployment and requiring new alignment strategies.

RANK_REASON The cluster contains a research paper detailing novel findings on LLM safety limitations.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM Safety Fails to Transfer Across Low-Resource Languages, Study Finds

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The cluster contains a research paper detailing novel findings on LLM safety limitations.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Abigail Oppong, P Sam Sahil, Tadesse Destaw Belay, Maryam Ibrahim Mukhtar, Esmael Ahmed Abdu, Tassallah Abdullahi, Jessica Oparebea, Saminu Mohammad Aliyu, Idris Abdulmumin, Abubakar Juma Chilala, Nicholaus Dismas Ladislaus, Alfred Malengo Kondoro, Lemof… ·

    The Illusion of Cross-Lingual Safety in Low-Resource Languages

    arXiv:2608.11146v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is largely developed in English, assuming these safeguards generalize across multilingual settings. However, this assumption remains underexplored and exposes a vulnerability in low-r…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    The Illusion of Cross-Lingual Safety in Low-Resource Languages

    Safety alignment in large language models (LLMs) is largely developed in English, assuming these safeguards generalize across multilingual settings. However, this assumption remains underexplored and exposes a vulnerability in low-resource languages. We investigate cross-lingual …

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