在大型语言模型中利用内部激活实现稳定且可解释的人格特质评估
Stable and Explainable Personality Trait Evaluation in Large Language Models with Internal Activations
AI总结：
本文提出PVNI方法，通过内部激活提取人格向量并插值得分，实现LLMs中稳定且可解释的人格特质评估。
摘要：
在大型语言模型（LLMs）中评估人格特质是模型解释、比较和负责任部署的关键。然而，现有的基于问卷的评估方法存在稳定性差和可解释性低的问题，因为其结果对提示语的微小变化或角色扮演配置的变化非常敏感。为了解决这些限制，我们提出了一种基于内部激活的方法，称为人格向量中性插值（PVNI），用于在LLMs中实现稳定且可解释的人格特质评估。PVNI通过对比提示从模型的内部激活中提取与目标人格特质相关的个性向量。然后通过沿个性向量作为锚轴进行插值来估计相应的中性分数，从而在中性提示表示和个性方向之间实现可解释的比较。我们提供了PVNI有效性和泛化性质的理论分析。在多样化的LLMs上进行的广泛实验表明，PVNI比现有方法产生更加稳定的个性特质评估，即使在问卷和角色扮演变体下也是如此。
英文摘要：
Evaluating personality traits in Large Language Models (LLMs) is key to model interpretation, comparison, and responsible deployment. However, existing questionnaire-based evaluation methods exhibit limited stability and offer little explainability, as their results are highly sensitive to minor variations in prompt phrasing or role-play configurations. To address these limitations, we propose an internal-activation-based approach, termed Persona-Vector Neutrality Interpolation (PVNI), for stable and explainable personality trait evaluation in LLMs. PVNI extracts a persona vector associated with a target personality trait from the model's internal activations using contrastive prompts. It then estimates the corresponding neutral score by interpolating along the persona vector as an anchor axis, enabling an interpretable comparison between the neutral prompt representation and the persona direction. We provide a theoretical analysis of the effectiveness and generalization properties of PVNI. Extensive experiments across diverse LLMs demonstrate that PVNI yields substantially more stable personality trait evaluations than existing methods, even under questionnaire and role-play variants.
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