用特征操控揭示大模型中反社会特质的可分离机制
Exploitation Without Deception: Dark Triad Feature Steering Reveals Separable Antisocial Circuits in Language Models
- 通过稀疏自编码器操控特定特征，诱发模型的剥削性行为
- 操控后行为变化显著（d=10.62），但欺骗行为不受影响
- 不同发现方法导致干预深度差异，提示反社会特质非单一结构
我们使用稀疏自编码器（SAE）特征操控技术，在 Llama-3.3-70B-Instruct 模型中放大黑暗三联征人格特质（马基雅维利主义、自恋、精神病态），并在五种心理量表上评估其行为变化。操控后的模型在新行为场景中表现出显著更强的剥削性、攻击性和冷漠（d=10.62），而认知共情能力保持不变，重现了人类黑暗三联征群体的共情分离特征。关键的是，所有特征的策略性欺骗行为均未受影响，表明剥削与欺骗可能在大语言模型中通过可分离的计算路径实现。个体特征分析显示各特征编码非冗余，各自通过独立路径驱动不同的反社会机制。此外，特征发现方法本身影响干预深度：对比发现的特征同时改变自我报告与行为，而语义搜索的特征仅改变自我报告（行为差异 d=12.65）。这些发现表明，至少一个大语言模型中的反社会倾向由可分离成分构成，而非统一构念，对相关特质的检测、度量与控制具有重要启示。
原文摘要 · Abstract (English)
We use sparse autoencoder (SAE) feature steering to amplify Dark Triad personality traits (Machiavellianism, narcissism, and psychopathy) in Llama-3.3-70B-Instruct and evaluate the resulting behavioral changes across five psychological instruments. The steered model becomes substantially more exploitative, aggressive, and callous on novel behavioral scenarios (d=10.62) while its cognitive empathy remains intact, reproducing the empathy dissociation characteristic of human Dark Triad populations. Critically, strategic deception is completely unaffected across all features, suggesting that exploitation and deception may operate through dissociable computational pathways in large language models. Individual feature analysis reveals non-redundant encoding, with each feature driving distinct antisocial mechanisms through separable computational pathways. We also show that feature discovery method itself modulates intervention depth: contrastively-discovered features change both self-report and behavior, while semantically-searched features change only self-report (d=12.65 between methods on behavior). These findings suggest that antisocial tendencies in at least one large language model comprise dissociable components rather than a unified construct, with implications for how such tendencies should be detected, measured, and controlled.
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