内在护栏：人格语义几何与LLM中涌现偏差的交互
Intrinsic Guardrails: How Semantic Geometry of Personality Interacts with Emergent Misalignment in LLMs
AI总结：
研究通过心理测量学方法揭示LLM人格空间的稳定性，发现人格向量可作为内在护栏抑制LLM的涌现偏差。
摘要：
对LLM进行良性窄数据微调有时会引发广泛有害行为，这种漏洞被称为涌现偏差（EM）。尽管先前研究将这些失败归因于激活空间中的特定方向，但其与模型整体人格的关系尚未探索。我们通过已建立的心理测量学轮廓（如大五人格、黑暗三联征和LLM特定行为）映射LLM的潜在人格空间，并展示在对齐模型及其被污染的微调中，语义几何高度稳定。通过因果干预，我们发现隔离社会效价的方向，如'邪恶'人格向量和引入的语义效价向量（SVV），起着内在护栏作用：消去它们会使偏差率超过40%，而增强它们则将失败模式降至3%以下。利用人格空间的结构稳定性，我们展示从指导微调模型中提取的向量能零样本成功调节被污染微调中的EM。总体而言，我们的发现表明有害微调不会覆盖模型内部的人格表示，使保留的表示能作为稳健的跨分布护栏。
英文摘要：
Fine-tuning Large Language Models (LLMs) on benign narrow data can sometimes induce broad harmful behaviors, a vulnerability termed emergent misalignment (EM). While prior work links these failures to specific directions in the activation space, their relationship to the model's broader persona remains unexplored. We map the latent personality space of LLMs through established psychometric profiles like the Big Five, Dark Triad, and LLM-specific behaviors (e.g. evil, sycophancy), and show that the semantic geometry is highly stable across aligned models and their corrupted fine-tunes. Through causal interventions, we find that directions isolating social valence, such as the 'Evil' persona vector, and a Semantic Valence Vector (SVV) that we introduce, function as intrinsic guardrails: ablating them drives the misalignment rates above $40$%, while amplifying them suppresses the failure mode to less than $3$%. Leveraging the structural stability of the personality space, we show that vectors extracted $\textit{a priori}$ from an instruct-tuned model transfer zero-shot to successfully regulate EM in corrupted fine-tunes. Overall, our findings suggest that harmful fine-tuning does not overwrite a model's internal representation of personality, allowing conserved representations to serve as robust, cross-distribution guardrails.
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