PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra
arXiv:2602.15669v1 Announce Type: new Abstract: Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning, failing to capture the dynamic and compositional nature of human traits. We introduce PERSONA, a training-free framework that achieves fine-tuning level performance through direct manipulation of personality vectors in activation space. Our key insight is that personality traits appear as extractable, approximately orthogonal directions in the model's representation space that support algebraic operations. The framework operates through three stages: Persona-Base extracts orthogonal trait vectors via contrastive activation analysis; Persona-Algebra enables precise control through vector arithmetic (scalar multiplication for intensity, addition for composition, subtraction for suppression); and Persona-Flow achieves context-aware adaptation by dynamically composing these vectors during inference. On PersonalityBench, our app
arXiv:2602.15669v1 Announce Type: new Abstract: Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning, failing to capture the dynamic and compositional nature of human traits. We introduce PERSONA, a training-free framework that achieves fine-tuning level performance through direct manipulation of personality vectors in activation space. Our key insight is that personality traits appear as extractable, approximately orthogonal directions in the model's representation space that support algebraic operations. The framework operates through three stages: Persona-Base extracts orthogonal trait vectors via contrastive activation analysis; Persona-Algebra enables precise control through vector arithmetic (scalar multiplication for intensity, addition for composition, subtraction for suppression); and Persona-Flow achieves context-aware adaptation by dynamically composing these vectors during inference. On PersonalityBench, our approach achieves a mean score of 9.60, nearly matching the supervised fine-tuning upper bound of 9.61 without any gradient updates. On our proposed Persona-Evolve benchmark for dynamic personality adaptation, we achieve up to 91% win rates across diverse model families. These results provide evidence that aspects of LLM personality are mathematically tractable, opening new directions for interpretable and efficient behavioral control.
Executive Summary
The 'PERSONA' framework presents a significant advancement in personality control for Large Language Models (LLMs). By leveraging activation vector algebra, PERSONA achieves fine-tuning level performance without requiring extensive training or fine-tuning. The framework operates through three stages: Persona-Base extracts orthogonal trait vectors, Persona-Algebra enables precise control, and Persona-Flow achieves context-aware adaptation. The results demonstrate PERSONA's efficacy in capturing dynamic and compositional aspects of human traits, with mean scores comparable to supervised fine-tuning. This research opens new avenues for interpretable and efficient behavioral control in LLMs, with potential applications in various domains, including human-computer interaction, education, and mental health.
Key Points
- ▸ PERSONA achieves fine-tuning level performance without training or fine-tuning
- ▸ The framework operates through three stages: Persona-Base, Persona-Algebra, and Persona-Flow
- ▸ PERSONA demonstrates efficacy in capturing dynamic and compositional aspects of human traits
Merits
Strength
PERSONA's ability to achieve fine-tuning level performance without training or fine-tuning is a significant advancement in personality control for LLMs.
Interpretability
PERSONA's reliance on activation vector algebra provides a mathematically tractable approach to behavioral control, enabling interpretable results.
Demerits
Limited scope
The current implementation of PERSONA may be limited to specific model families and may not be generalizable to all LLMs.
Lack of human evaluation
The evaluation of PERSONA's performance relies on automated metrics, which may not fully capture the nuances of human perception and interpretation.
Expert Commentary
The 'PERSONA' framework presents a groundbreaking approach to personality control for LLMs, leveraging activation vector algebra to achieve fine-tuning level performance without training or fine-tuning. This research has significant implications for the development of LLMs in various domains, including human-computer interaction, education, and mental health. However, the current implementation may be limited to specific model families, and the evaluation of PERSONA's performance relies on automated metrics. As such, further research is needed to ensure that PERSONA can be applied more broadly and that its performance is evaluated in a more comprehensive manner.
Recommendations
- ✓ Future research should focus on developing PERSONA for a wider range of LLM families and evaluating its performance using a more comprehensive set of metrics.
- ✓ Developing regulatory frameworks to ensure responsible AI development and mitigate the potential risks of biased or manipulative behavior in LLMs is essential.
