← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1395 · 记录 #1
PersonalityDBench: A Dataset for Personality Disorders - from Modeling to Controlled Generation
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:53
摘要
Personality disorders (PDs) are a complex class of mental health (MH) conditions characterized by persistent patterns of cognition, behavior, and emotional regulation that deviate from cultural norms. While social media has become a valuable resource for MH research, NLP has largely focused on more prevalent conditions (e.g., depression), leaving PDs underexplored. In this work, we introduce PersonalityDBench, a large-scale, clinically grounded dataset that supports multidimensional study of personality pathology, and standardized, reproducible evaluation of LLM steering toward clinically grounded behavioral targets. The dataset comprises two parts: (1) PRISMA and (2) PersonaDSteering. (1) PRISMA (PeRsonality dISorder MAnifestations) is a clinically annotated collection of social media content spanning the full spectrum of PDs. It links clinically validated diagnostic criteria and dimensional trait frameworks with computational annotation and analysis methods to support fine-grained, multidimensional study of how PDs manifests in naturalistic, free-form language. Building on PRISMA, (2) PersonaDSteering is a benchmark for LLM steering evaluation that operationalizes clinically grounded PD profiles into structured behavioral elicitation tasks, enabling multidimensional steerability assessment beyond single-behavior settings and supporting PD-consistent persona construction for simulated patient generation. This dataset may have application in the study and modeling of PD and powering personality-specific text generation for adaptive, personalized chat systems.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
PersonalityDBench: A Dataset for Personality Disorders - from Modeling to Controlled Generation
Personality disorders (PDs) are a complex class of mental health (MH) conditions characterized by persistent patterns of cognition, behavior, and emotional regulation that deviate from cultural norms. While social media has become a valuable resource for MH research, NLP has largely focused on more prevalent conditions (e.g., depression), leaving PDs underexplored. In this work, we introduce PersonalityDBench, a large-scale, clinically grounded dataset that supports multidimensional study of personality pathology, and standardized, reproducible evaluation of LLM steering toward clinically grounded behavioral targets. The dataset comprises two parts: (1) PRISMA and (2) PersonaDSteering. (1) PRISMA (PeRsonality dISorder MAnifestations) is a clinically annotated collection of social media content spanning the full spectrum of PDs. It links clinically validated diagnostic criteria and dimensional trait frameworks with computational annotation and analysis methods to support fine-grained, multidimensional study of how PDs manifests in naturalistic, free-form language. Building on PRISMA, (2) PersonaDSteering is a benchmark for LLM steering evaluation that operationalizes clinically grounded PD profiles into structured behavioral elicitation tasks, enabling multidimensional steerability assessment beyond single-behavior settings and supporting PD-consistent persona construction for simulated patient generation. This dataset may have application in the study and modeling of PD and powering personality-specific text generation for adaptive, personalized chat systems.
查看此版本解读 →查看此版本原文 ↗