大语言模型角色中的情绪劳动策略偏好
Emotional Labor Strategy Preferences in LLM Personas
- University of Cincinnati（辛辛那提大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究构建含500个场景的情绪劳动策略数据集，发现注入人格角色的大语言模型更倾向深度扮演，尽责性与情绪稳定性可预测该偏好，且角色会影响模型输出。
AI中文摘要：
情绪劳动是指为满足社会或职业期望而付出努力管理情绪表现的行为。人格特质与情绪劳动策略存在关联，但相关研究几乎仅依赖职业场景下的自我报告量表。本研究探究注入基于心理测量学的角色的大语言模型（LLM）是否会在日常社交场景中复现这种由人格驱动的选择模式。我们构建了首个包含500个社交场景事件的情绪劳动策略数据集，每个事件提供表面扮演、深度扮演和真实表达三种行为选择。我们从大型人格库中选取50个虚构角色，通过两条平行路径对其进行刻画：观察者评定的双极形容词组合和角色内自我报告条目。五个LLM在两种角色条件下评估所有场景。我们发现模型更倾向于深度扮演，且尽责性和情绪稳定性可一致预测该偏好。熵分析证实角色可靠影响输出，且在不同模型和情绪间存在差异。
英文摘要：
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.
