arXiv AI
Evaluating the Efficacy of LLMs to Emulate Realistic Human Personalities
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arXiv AI
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Beyond BFI: The CSI for Enhanced Reliability and Validity in Evaluating LLM Personality Traits
The paper introduces the Core Sentiment Inventory (CSI), a new personality trait evaluation tool for large language models (LLMs) that addresses reliability and validity issues found in existing methods like the Big Five Inventory (BFI). CSI is designed specifically for LLMs, supports both English and Chinese, and provides detailed psychological portraits of model behavior. Experiments show that CSI captures nuanced behavioral patterns, improves reliability, and correlates strongly (above 0.85) with real-world LLM outputs.
arXiv Computation and Language
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Emotional Labor Strategy Preferences in LLM Personas
arXiv:2609.00310v1 Announce Type: new Abstract: Emotional labor is the effortful management of emotional displays to meet social or professional expectations. Personality traits have been correlated...
arXiv AI
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Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior
arXiv:2608. 16196v1 Announce Type: new Abstract: Personalized game generation requires inferring a player's abilities and behavioral style from how they play.
arXiv AI
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Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests
arXiv:2510. 22170v3 Announce Type: replace Abstract: Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure or superficial variation.
arXiv Computer Vision
Aug 25
Traits Run Deeper: Trait-Specific Asymmetric Fusion for Personality Assessment
arXiv:2606.11269v2 Announce Type: replace Abstract: Personality assessment aims to infer stable traits from dynamic behaviors across modalities like language, voice, and facial expressions. Existing...
arXiv AI
Aug 10
Do AI Personas Grow? Analyzing and Benchmarking Personality Evolution in LLM Agents After Life Events
arXiv:2608. 06485v1 Announce Type: cross Abstract: Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing, motivating the development of lifelong agents that remain coherent over extended interactions.