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    <abstract>Evaluating personality-related tendencies in Large Language Models (LLMs) helps characterize model behavior, compare models beyond task accuracy, and support responsible deployment in socially interactive settings. 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.</abstract>
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%0 Conference Proceedings
%T Stable and Explainable Personality Trait Evaluation in Large Language Models with Internal Activations
%A Ma, Xiaoxu
%A Zhang, Xiangbo
%A Weng, Zhenyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ma-etal-2026-stable
%X Evaluating personality-related tendencies in Large Language Models (LLMs) helps characterize model behavior, compare models beyond task accuracy, and support responsible deployment in socially interactive settings. 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.
%U https://aclanthology.org/2026.findings-acl.803/
%P 16322-16340

Markdown (Informal)

[Stable and Explainable Personality Trait Evaluation in Large Language Models with Internal Activations](https://aclanthology.org/2026.findings-acl.803/) (Ma et al., Findings 2026)

• Stable and Explainable Personality Trait Evaluation in Large Language Models with Internal Activations (Ma et al., Findings 2026)

ACL

• Xiaoxu Ma, Xiangbo Zhang, and Zhenyu Weng. 2026. Stable and Explainable Personality Trait Evaluation in Large Language Models with Internal Activations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 16322–16340, San Diego, California, United States. Association for Computational Linguistics.