Users show curiosity over concern toward LLM inferences of personal information, with acceptability depending on context, alignment with expectations, and who uses the inferences rather than just the content.
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The paper introduces a new taxonomy that groups AI-driven psychological computing tasks by their underlying computational patterns into four categories and reviews over 300 works from the pre-trained model to LLM eras.
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.
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.
Dark personality traits predict self-reported online incivility but show no reliable link to linguistic toxicity features extracted from users' actual Reddit activity.
citing papers explorer
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When Are LLM Inferences Acceptable? User Reactions and Control Preferences for Inferred Personal Information
Users show curiosity over concern toward LLM inferences of personal information, with acceptability depending on context, alignment with expectations, and who uses the inferences rather than just the content.
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From Pre-trained Models to Large Language Models: A Comprehensive Survey of AI-Driven Psychological Computing
The paper introduces a new taxonomy that groups AI-driven psychological computing tasks by their underlying computational patterns into four categories and reviews over 300 works from the pre-trained model to LLM eras.
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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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Dark Personality Traits and Online Toxicity: Linking Self-Reports to Reddit Activity
Dark personality traits predict self-reported online incivility but show no reliable link to linguistic toxicity features extracted from users' actual Reddit activity.