In this paper, we investigate how personalising Large Language Models (Persona-LLMs) with annotator personas affects their sensitivity to hate speech, particularly regarding biases linked to shared or differing identities between annotators and targets. To this end, we employ Google's Gemini and OpenAI's GPT-4.1-mini models and two persona-prompting methods: shallow persona prompting and a deeply contextualised persona development based on Retrieval-Augmented Generation (RAG) to incorporate richer persona profiles. We analyse the impact of using in-group and out-group annotator personas on the models' detection performance and fairness across diverse social groups. This work bridges psychological insights on group identity with advanced NLP techniques, demonstrating that incorporating socio-demographic attributes into LLMs can address bias in automated hate speech detection. Our results highlight both the potential and limitations of persona-based approaches in reducing bias, offering valuable insights for developing more equitable hate speech detection systems.

翻译：本文研究了通过标注者人物角色个性化大语言模型（Persona-LLMs）如何影响其对仇恨言论的敏感性，特别是关于标注者与目标对象之间身份认同（共享或相异）相关的偏见。为此，我们采用了谷歌的Gemini和OpenAI的GPT-4.1-mini模型，并运用两种人物角色提示方法：浅层人物角色提示和基于检索增强生成（RAG）的深度情境化人物角色构建，以融入更丰富的人物角色画像。我们分析了使用组内与组外标注者人物角色对模型在不同社会群体中的检测性能与公平性的影响。此项工作将群体认同的心理学洞见与先进的自然语言处理技术相结合，证明将社会人口统计学属性融入大语言模型可以解决自动化仇恨言论检测中的偏见问题。我们的结果既凸显了基于人物角色方法在减少偏见方面的潜力，也揭示了其局限性，为开发更公平的仇恨言论检测系统提供了有价值的见解。