Do Personality Traits Interfere? Geometric Limitations of Steering in Large Language Models
arXiv:2602.15847v1 Announce Type: cross Abstract: 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
arXiv:2602.15847v1 Announce Type: cross Abstract: 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.
Executive Summary
This article examines the geometric relationships between Big Five personality steering directions in large language models, revealing substantial geometric dependence between traits. The findings suggest that personality traits occupy a slightly coupled subspace, limiting fully independent trait control. The study analyzed steering vectors from two model families and applied various geometric conditioning schemes, concluding that hard orthonormalisation can enforce geometric independence but does not eliminate cross-trait effects.
Key Points
- ▸ Personality steering directions exhibit substantial geometric dependence
- ▸ Steering one trait induces changes in others, even with linear overlap removal
- ▸ Hard orthonormalisation enforces geometric independence but reduces steering strength
Merits
Novel Approach
The study's geometric conditioning schemes provide a new perspective on personality steering in LLMs
Demerits
Limited Generalizability
The findings may not be applicable to all LLMs or personality traits, as the study only examined two model families
Expert Commentary
The article's findings have significant implications for the development of more sophisticated and nuanced LLMs. By recognizing the coupled nature of personality traits, researchers can design more effective and targeted steering mechanisms. However, this will require a deeper understanding of the geometric relationships between traits and the development of more advanced geometric conditioning schemes. Ultimately, this research contributes to the ongoing effort to create more transparent, explainable, and controllable AI systems.
Recommendations
- ✓ Future studies should investigate the generalizability of these findings to other LLMs and personality traits
- ✓ Developers should prioritize the development of more advanced geometric conditioning schemes to mitigate cross-trait effects
