EERPD：利用情绪与情绪调节改进人格检测
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection
AI总结：
本研究针对现有基于文本的人格检测忽略心理学知识的问题，提出融合情绪调节与情绪特征、结合少样本示例和思维链的EERPD方法，显著提升了人格检测的准确率与鲁棒性。
摘要：
人格是心理学中的一个基础构念，反映个体的行为、思维与情绪模式。过往研究在人格检测领域已取得一定进展，主要通过利用整段文本预测人格，但这类研究普遍倾向于忽略心理学知识：极少应用情绪调节与人格之间已被充分证实的关联。基于此，我们提出一种名为EERPD的新型人格检测方法，该方法引入与人格高度相关的心理学概念——情绪调节——用于人格预测。通过将该特征与情绪特征结合，方法检索少样本示例并提供过程思维链（CoTs）以从文本推断标签，增强了大语言模型（LLM）对文本中人格的理解，提升了人格检测性能。实验结果表明，EERPD显著提升了人格检测的准确率与鲁棒性，在两个基准数据集上的平均F1值较此前的最优水平（SOTA）分别提升15.05和4.29。
英文摘要：
Personality is a fundamental construct in psychology, reflecting an individual's behavior, thinking, and emotional patterns. Previous researches have made some progress in personality detection, primarily by utilizing the whole text to predict personality. However, these studies generally tend to overlook psychological knowledge: they rarely apply the well-established correlations between emotion regulation and personality. Based on this, we propose a new personality detection method called EERPD. This method introduces the use of emotion regulation, a psychological concept highly correlated with personality, for personality prediction. By combining this feature with emotion features, it retrieves few-shot examples and provides process CoTs for inferring labels from text. This approach enhances the understanding of LLM for personality within text and improves the performance in personality detection. Experimental results demonstrate that EERPD significantly enhances the accuracy and robustness of personality detection, outperforming previous SOTA by 15.05/4.29 in average F1 on the two benchmark datasets.
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