arXiv cs.AI论文
安全对齐的幻觉：大语言模型中的跨语言安全差距
该研究指出当前大语言模型的安全对齐训练以英语为中心，导致非英语语言存在安全漏洞。作者提出了INCLUDE基准，包含2604个提示，覆盖六种印度语言，评估十个开源和闭源模型，发现孟加拉语在开源模型中偏见得分最高，而英语在闭源模型中偏见最高，揭示了跨语言安全对齐的不足。
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Abstract:Current safety alignment training for Large Language Models (LLMs) are heavily English-centric. When such safety filters fail for non-English languages, the consequences are immediate and user-facing: voice assistants and spoken dialogue systems may produce stereotype-reinforcing outputs, bypassing the standard English-focused safety alignments and propagating harmful bias to non-English speaking communities. For spoken language technologies deployed across India's linguistically diverse population, this represents a critical failure mode. To address this cross-lingual gap, we introduce INCLUDE (Indian Cultural Lens for Understanding and Detecting Embedded Biases), a multilingual evaluation benchmark designed to quantify Indian-centric socio-cultural biases. INCLUDE consists of 2,604 prompts spanning six prompt languages: English, Hindi, Bengali, Marathi, Tamil, and Hinglish (Hindi-English code-mix). We evaluate ten open- and closed-source LLMs against this benchmark, analyzing 14,988 bias scores. Our statistical results reveal two key findings. First, Bengali yielded the highest average bias score in open-source models. Second, English demonstrated a notable reversal, producing the lowest bias in open-source models but the highest bias in closed-source models.