标题： 揭示LLMs中的隐藏暴力倾向：通过行为情景进行的人口统计分析 显示英文标题

标题： Uncovering Hidden Violent Tendencies in LLMs: A Demographic Analysis via Behavioral Vignettes

摘要： 大型语言模型（LLMs）越来越多地被提出用于检测和应对在线暴力内容，但它们在推理道德模糊、现实世界场景方面的能力仍缺乏深入研究。 我们进行了第一个研究，使用经过验证的社会科学工具来评估LLMs，该工具旨在衡量人类对日常冲突的反应，即暴力行为情景问卷（VBVQ）。 为了评估潜在的偏见，我们引入了基于角色的提示，该提示在美国范围内变化种族、年龄和地理身份。 在统一的零样本设置下，评估了六个在不同地缘政治和组织背景下开发的LLMs。 我们的研究揭示了两个关键发现：（1）LLMs表层文本生成往往与其对暴力回应的内部偏好相偏离；（2）它们的暴力倾向在不同人口统计学中有所变化，经常与犯罪学、社会科学和心理学中的既有发现相矛盾。 显示英文摘要

摘要： Large language models (LLMs) are increasingly proposed for detecting and responding to violent content online, yet their ability to reason about morally ambiguous, real-world scenarios remains underexamined. We present the first study to evaluate LLMs using a validated social science instrument designed to measure human response to everyday conflict, namely the Violent Behavior Vignette Questionnaire (VBVQ). To assess potential bias, we introduce persona-based prompting that varies race, age, and geographic identity within the United States. Six LLMs developed across different geopolitical and organizational contexts are evaluated under a unified zero-shot setting. Our study reveals two key findings: (1) LLMs surface-level text generation often diverges from their internal preference for violent responses; (2) their violent tendencies vary across demographics, frequently contradicting established findings in criminology, social science, and psychology.