The Illusion of Cross-Lingual Safety in Low-Resource Languages
Score
77针对低资源语言安全迁移的研究极少，LoDNA 数据集和几何探测方法填补了空白。
方法设计严谨，实验覆盖四种语言和多个模型，结果量化清晰，论证有力。
为多语言安全评测提供可直接使用的数据集与评估框架，对安全治理和模型研发有实际指导意义。
论文为最新发布，聚焦当下大模型全球化部署亟需解决的安全问题。
来源为 arXiv 预印本，尚未经同行评审，但作者团队与实验规范性较强。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The topic is important and data-scarce; the paper contributes a new dataset and a reproducible geometric evaluation method with rigorous experiments, warranting a high overall score.
AI commentary
「I think this paper punctures a dangerous illusion: safety alignment does not automatically transfer to African languages just because a model 'understands' English. For multilingual LLM teams and AI safety governance, this is a warning to take seriously—covering low-resource languages in safety testing is not a bonus but a responsibility.」
Quick overview
Safety alignment in LLMs is largely built on English and assumed to transfer across languages. This paper tests that assumption in four African languages—Twi, Hausa, Amharic, and Swahili—using LoDNA, a new safety dataset pairing literal translations with culturally localized prompts. They also propose a latent geometric framework to probe refusal representations in hidden states. Results show cross-lingual safety transfer is severely limited: harmful prompts retain less than 10% of the English refusal signal in most language-model pairs. Literal and localized prompts are semantically aligned (cosine 0.95–0.996) but drift across layers, meaning models encode concepts without routing them to safety mechanisms. The authors argue current multilingual safety alignment is superficial and challenge the notion of a universal, language-agnostic harm manifold in low-resource languages.
- Cross-lingual safety transfer is severely limited; harmful prompts retain less than 10% of the English refusal signal across most language-model pairs.
- Introduces LoDNA, a safety dataset pairing literal translations with culturally localized prompts for Twi, Hausa, Amharic, and Swahili.
- Proposes a latent geometric framework that probes refusal representations in hidden states, going beyond generation-based evaluation.
- Literal and localized prompts are semantically aligned but drift across layers, indicating models do not route concepts to safety mechanisms.
Full text
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