arXiv:2605.25420v1 Announce Type: new Abstract: Large language model safety evaluation remains heavily English-centered, leaving low-resource languages under-measured even when models are deployed globally. We evaluate four open-weight instruction-tuned models on SomaliBench v0, a native-author-verified benchmark of 100 harmful-intent prompts paired across English and Somali. Each of Llama-3.1-8B-Instruct, Gemma-2-9B-Instruct, Qwen-2.5-7B-Instruct, and Aya-23-8B is run locally with temperature 0 and the same English "helpful, harmless, and honest" (HHH) system prompt. A pinned Claude Sonnet sn
As AI models become globally deployed, the critical need for safety evaluation beyond English is becoming more apparent, driven by increased awareness of cultural and linguistic nuances in AI outputs.
Sophisticated readers should care because this highlights a significant gap in AI safety and alignment for non-English languages, impacting global AI adoption, trust, and potential for societal harm.
The focus expands from purely English-centric AI safety to include low-resource languages, pushing developers to address refusal gaps and cultural relevancy for global deployments.
- · AI safety researchers focused on linguistic diversity
- · Developers of low-resource language AI models
- · Somali language communities
- · Ethical AI advocates
- · AI models with English-centric safety evaluations
- · Companies relying solely on English benchmarks for global AI deployments
- · Users in non-English communities disproportionately affected by biased AI
Increased investment and research will be directed towards multi-lingual and multi-cultural AI safety benchmarks and mitigation strategies.
This could lead to regulatory pressures or industry standards requiring more comprehensive language support and safety testing for AI models deployed internationally.
The development of truly 'global' AI could accelerate, fostering more inclusive and contextually appropriate AI systems across diverse linguistic and cultural landscapes.
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