A gamified system with multiple LLM agents of varied personalities gathers interaction data to produce more effective and interpretable Big Five personality assessments than single-context methods.
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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.
Personality specifications dominate AI agent social behaviors such as response length more than model choice or operational rules in a controlled deployment study.
citing papers explorer
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Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities
A gamified system with multiple LLM agents of varied personalities gathers interaction data to produce more effective and interpretable Big Five personality assessments than single-context methods.
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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.
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Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification
Personality specifications dominate AI agent social behaviors such as response length more than model choice or operational rules in a controlled deployment study.