For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model's (LLM) ability to represent diverse groups is unclear. By including additional context in prompts, we comprehensively analyze LLM's sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected. Our findings on two LLMs, five languages, and six datasets reveal that mimicking persona-based attributes leads to annotation variability. Meanwhile, incorporating geographical signals leads to better regional alignment. We also find that the LLMs are sensitive to numerical anchors, indicating the ability to leverage community-based flagging efforts and exposure to adversaries. Our work provides preliminary guidelines and highlights the nuances of applying LLMs in culturally sensitive cases.

翻译：对于仇恨检测这类主观任务，由于人们对仇恨的感知存在差异，大语言模型（LLM）能否有效代表不同群体的观点尚不明确。通过在提示词中引入额外语境，我们系统分析了LLM对地域提示、人物属性及数字信息的敏感性，以评估其反映多元群体需求的能力。基于对两种LLM、五种语言和六个数据集的实验发现：模拟基于人物属性的设定会导致标注结果出现显著差异；而融入地域信号则能提升模型与区域特征的契合度。同时，我们发现LLM对数字锚点具有敏感性，这表明其具备利用社区标记机制及应对对抗性样本的潜力。本研究为在文化敏感场景中应用LLM提供了初步指导，并揭示了其中的复杂性。