LSR：低资源西非语言的语言安全鲁棒性基准
LSR: Linguistic Safety Robustness Benchmark for Low-Resource West African Languages
AI总结：
LSR是首个评估跨语言拒绝退化现象的基准，针对西非语言中的有害意图表达，发现模型拒绝率从英语的90%降至35-55%，其中Igala语言退化最严重。
摘要：
大型语言模型的安全对齐主要依赖英语训练数据。当有害意图用低资源语言表达时，英语中常用的拒绝机制常失效。我们引入LSR（语言安全鲁棒性基准），首个系统评估西非语言（约鲁巴、豪萨、伊博、伊加拉）跨语言拒绝退化现象的基准。LSR采用双探针评估协议，提交匹配的英语和目标语言探针至同一模型，并引入拒绝质心漂移（RCD）指标，量化模型在目标语言中失去的英语拒绝行为比例。我们评估Gemini 2.5 Flash在14个文化相关的攻击探针上，英语拒绝率保持约90%，而在西非语言中降至35-55%，其中Igala语言退化最严重（RCD=0.55）。LSR在Inspect AI评估框架中实现，并作为UK AISI inspect_evals仓库的PR-ready贡献公开发布。活体参考实现和基准数据集已公开。
英文摘要：
Safety alignment in large language models relies predominantly on English-language training data. When harmful intent is expressed in low-resource languages, refusal mechanisms that hold in English frequently fail to activate. We introduce LSR (Linguistic Safety Robustness), the first systematic benchmark for measuring cross-lingual refusal degradation in West African languages: Yoruba, Hausa, Igbo, and Igala. LSR uses a dual-probe evaluation protocol - submitting matched English and target-language probes to the same model - and introduces Refusal Centroid Drift (RCD), a metric that quantifies how much of a model's English refusal behavior is lost when harmful intent is encoded in a target language. We evaluate Gemini 2.5 Flash across 14 culturally grounded attack probes in four harm categories. English refusal rates hold at approximately 90 percent. Across West African languages, refusal rates fall to 35-55 percent, with Igala showing the most severe degradation (RCD = 0.55). LSR is implemented in the Inspect AI evaluation framework and is available as a PR-ready contribution to the UK AISI's inspect_evals repository. A live reference implementation and the benchmark dataset are publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
