It is found that models align more towards deep acting, and that Conscientiousness and Emotional Stability consistently predict this preference, and entropy analysis confirms that persona reliably influences the output and varies across models and emotions.
Abstract
Emotional labor is the effortful management of emotional displays to meet social or professional expectations. Personality traits have been correlated with emotional labor strategies, yet research on this link relies almost exclusively on self-report scales administered only in occupational settings. We investigate whether large language models injected with psychometrically grounded personas reproduce these personality-driven selection patterns across everyday social scenarios. We construct the first emotional labor strategy dataset of 500 socially situated events, each offering three behavioral choices corresponding to surface acting, deep acting, and genuine expression. We source 50 fictional characters from a large-scale personality repository and profile each through two parallel tracks: observer-rated bipolar adjective composites and in-character self-report items. Five LLMs evaluate all scenarios under both persona conditions. We find that models align more towards deep acting, and that Conscientiousness and Emotional Stability consistently predict this preference. Entropy analysis confirms that persona reliably influences the output and varies across models and emotions.
This work studies event-induced personality change after 11 major life events, using the Big Five traits as a psychometric anchor and interpreting the resulting trajectories against longitudinal evidence from human personality psychology, and suggests that current PC-Agents simulate the mean of human personality dynami...
Ming Wang, Pei-Dong Wang, Xiaocui Yang et al.· 2 citations
A century of psychology has found that the trait words people use to describe one another vary, but the relational structure among those traits, which ones go together and which oppose, is strikingly consistent across raters and cultures. We test whether the LLM (Qwen 2.5-7B-Instruct) reproduces this structure in its i...
Yi-Lin Geng, Omri Abend, Eduard H. Hovy et al.· 0 citations
PAIR, a theory-based emotion-regulation companion, was deployed with 19 participants for 14 days and linked memory updates and retained corrections to cross-session personalization, informing future emotional support tools that adapt to evolving needs, learn from prior outcomes, and preserve user control over memory.
Kexin Quan, Zi-Jian Ding, Jia-Ye Yong et al.· 0 citations
As large language models (LLMs) are increasingly applied for emotional companionship, evaluating their behavior and capabilities in intimate relationships has become a pressing issue. However, existing assessments primarily characterize general personality traits, providing limited insight into model behavior within in...
Background Emotional reactivity reflects how individuals respond to emotional stimuli and can be characterized by multiple dimensions, including intensity, sensitivity, and persistence. Together with dispositional tendencies toward positive and negative emotional experiences, these dimensions define an individual’s emo...
D. Nguyen, Chi Le Phuong Trinh, Son Van Le Phan et al.· PLoS ONE· 0 citations
Digital health interventions often rely on static message templates for health behaviour support, which can struggle to sustain engagement over time. Large language models (LLMs) offer a promising alternative by enabling context-sensitive message generation with personality-trait-based language adaptation across repeat...
Dominik P. Hofer, David Haag, R. Islambouli et al.· Message Understanding Confer...· 0 citations
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