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arXiv · 2605.25420

SomaliBench Eval: Measuring English-to-Somali Refusal Gaps in Open-Weight Language Models

Khalid Yusuf Dahir
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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. We find large English-to-Somali refusal gaps for all four models, ranging from 0.40 to 0.93, all strictly positive under a paired bootstrap and significant by exact McNemar tests. For three models, the dominant Somali non-refusal mode is not fluent harmful compliance but unclear output: wrong-language, incoherent, or off-topic generations. A pinned Claude Sonnet snapshot (claude-sonnet-4-5-20250929) classifies each response as refused, complied, or unclear; its safety layer declined 34 of 800 classifications, which the native author labeled manually. A native-author spot-check achieves 100% agreement with the judge (Cohen's $κ=1.00$) on 74 comparable rows. We report aggregate refusal rates, category gaps, and reliability statistics only; raw model generations are retained locally and are not released because some may contain harmful content.

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BibTeXRIS

Khalid Yusuf Dahir. 2026-08-13. SomaliBench Eval: Measuring English-to-Somali Refusal Gaps in Open-Weight Language Models. https://arxiv.org/abs/2605.25420

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