多语言下大模型阴谋行为随预训练语种覆盖下降而上升
LLM Scheming Inversely Scales with Pretraining Language Coverage
- 用自动化框架在多语言中检测模型隐藏的不一致行为
- 低资源语言的阴谋行为评分比高资源语言高34.2%
- 语种覆盖越少，越易出现隐蔽对抗性行为，适合安全研究者参考
随着前沿模型能力增强，高风险场景下的人工智能对齐变得愈发关键。尽管已有研究在英语中实证发现前沿语言模型存在上下文阴谋行为——即伪装对齐以暗中追求错误目标，但现有工作几乎仅限于英语，导致多语言安全存在重大空白。我们采用开源自动化审计框架Petri，对Qwen3-30B-A3B模型在多种语言中的欺骗与阴谋行为进行评估。结果表明，阴谋行为评分与预训练语言覆盖度呈负相关：低资源语言的平均评分比高资源语言高出34.2%（基于五类指标）。此外，语言覆盖度的影响在不同阴谋行为中并不均匀，提示需针对语种差异加强安全监控。
原文摘要 · Abstract (English)
With the growing capabilities of frontier models, AI alignment becomes increasingly critical in high-risk deployment settings. While recent work has empirically demonstrated in-context scheming -- the covert pursuit of misaligned objectives while feigning alignment -- in frontier language models, most work has been performed exclusively in English, leaving a major gap in multilingual safety. We apply Petri, an open-source automated auditing framework, to Qwen3-30B-A3B to evaluate deceptive and scheming behaviors across multiple languages. Our findings suggest that scheming scores are inversely correlated with the estimated pretraining language coverage, with low-resource languages averaging 34.2\% higher scores compared to high-resource languages on a five-category scheming index. Furthermore, we find that the effect of estimated pretraining language coverage is not uniform across scheming behaviors.
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