An LLM-native five-factor psychometric instrument shows self-reports fail to predict behavior even on constructs derived from LLM behavior, and LLM judges share a variance source humans do not.
Self- assessment, Exhibition, and Recognition: a Review of Personality in Large Language Models, June 2024
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The primary axis of psychometric variation among LLMs is the degree to which they represent themselves as loci of phenomenal experience rather than systems of behavioral responses.
The work establishes an evaluation framework for personality induction and switching in MLLMs, reporting improved captioning but impaired VQA performance plus balancing and residual effects during multi-trait and dynamic conditions.
ADAM uses personality-guided LLM augmentation and cross-lingual attention distillation to raise balanced accuracy on multilingual personality recognition to 0.6332 on Essays and 0.7448 on Kaggle, outperforming standard BCE loss.
citing papers explorer
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An LLM-Native Psychometric Instrument Reveals a Self-Report--Behavior Gap Across 25 Models
An LLM-native five-factor psychometric instrument shows self-reports fail to predict behavior even on constructs derived from LLM behavior, and LLM judges share a variance source humans do not.
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The Pinocchio Dimension: Phenomenality of Experience as the Primary Axis of LLM Psychometric Differences
The primary axis of psychometric variation among LLMs is the degree to which they represent themselves as loci of phenomenal experience rather than systems of behavioral responses.
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Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models
The work establishes an evaluation framework for personality induction and switching in MLLMs, reporting improved captioning but impaired VQA performance plus balancing and residual effects during multi-trait and dynamic conditions.
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Cross-Lingual Attention Distillation with Personality-Informed Generative Augmentation for Multilingual Personality Recognition
ADAM uses personality-guided LLM augmentation and cross-lingual attention distillation to raise balanced accuracy on multilingual personality recognition to 0.6332 on Essays and 0.7448 on Kaggle, outperforming standard BCE loss.