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arXiv 2602.15847cs.CLcs.AIcs.LG

人格特质会干扰吗?大型语言模型中的转向几何限制

Do Personality Traits Interfere? Geometric Limitations of Steering in Large Language Models

Network Analysis and Social Influence Modelling (NASIM) Lab(网络分析与社会影响建模实验室);School of Physics Maths and Computing(物理数学计算学院);The University of Western Australia(西澳大学);School of Computing(计算学院);Macquarie University(麦觉瑞大学)

Pranav Bhandari, Usman Naseem, Mehwish Nasim

AI总结:

本研究发现大型语言模型中的人格特质存在几何依赖,即使去除线性重叠,转向一个特质仍会影响其他特质,限制了独立控制。

摘要:

在大型语言模型(LLMs)中,人格转向通常依赖于注入特定特质的转向向量,隐含假设人格特质可以独立控制。在本工作中,我们通过分析大五人格转向方向的几何关系来检验这一假设是否成立。我们研究了来自两个模型家族(LLaMA-3-8B和Mistral-8B)的转向向量,并应用了多种几何条件方案,从无约束方向到软性和硬性正交化。我们的结果表明,人格转向方向表现出显著的几何依赖性:转向一个特质会一致地引起其他变化,即使线性重叠被显式去除。虽然硬正交化强制几何独立性,但并不能消除跨特质行为效应,且可能降低转向强度。这些发现表明,LLM中的人格特质占据了一个略微耦合的子空间,限制了完全独立的特质控制。

英文摘要:

Personality steering in large language models (LLMs) commonly relies on injecting trait-specific steering vectors, implicitly assuming that personality traits can be controlled independently. In this work, we examine whether this assumption holds by analysing the geometric relationships between Big Five personality steering directions. We study steering vectors extracted from two model families (LLaMA-3-8B and Mistral-8B) and apply a range of geometric conditioning schemes, from unconstrained directions to soft and hard orthonormalisation. Our results show that personality steering directions exhibit substantial geometric dependence: steering one trait consistently induces changes in others, even when linear overlap is explicitly removed. While hard orthonormalisation enforces geometric independence, it does not eliminate cross-trait behavioural effects and can reduce steering strength. These findings suggest that personality traits in LLMs occupy a slightly coupled subspace, limiting fully independent trait control.

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。

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