性格在英语和印地语中影响人物条件化大语言模型叙事中的性别偏见：一项实证研究
Personality Shapes Gender Bias in Persona-Conditioned LLM Narratives Across English and Hindi: An Empirical Investigation
AI总结：
本研究探讨了在英语和印地语中，人物条件化大语言模型叙事中的性别偏见如何受到性格特征的影响，发现性格特质与性别偏见的幅度和方向显著相关，特别是黑暗三联体性格特质与性别刻板印象的表示更相关，但这些关联在不同模型和语言中有所变化。
摘要：
大型语言模型（LLMs）正越来越多地应用于以人物为导向的应用程序，如教育、客户服务和社会平台，在这些应用中，模型在与用户交互时被提示采用特定的人物。虽然人物条件可以提高用户体验和参与度，但也引发了关于性格线索如何与性别偏见和刻板印象相互作用的担忧。在本工作中，我们对英语和印地语中的人物条件化故事生成进行了受控研究，每个故事描绘了一名印度职场人士在系统性变化的人物性别、职业角色和性格特征（来自HEXACO和黑暗三联体框架）下生成特定情境的物品（例如教案、报告、信件）。在来自六种最先进的LLM生成的23,400个故事中，我们发现性格特征与性别偏见的幅度和方向显著相关。特别是，黑暗三联体性格特征与比社会可取的HEXACO特征更高的性别刻板印象表示相关，尽管这些关联在不同模型和语言中有所变化。我们的发现表明，LLM中的性别偏见并非静态，而是依赖于情境的。这表明在现实应用中使用的人物条件化系统可能会引入不均等的表示伤害，强化生成的教育、职业或社交内容中的性别刻板印象。
英文摘要：
Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where models are prompted to adopt specific personas when interacting with users. While persona conditioning can improve user experience and engagement, it also raises concerns about how personality cues may interact with gender biases and stereotypes. In this work, we present a controlled study of persona-conditioned story generation in English and Hindi, where each story portrays a working professional in India producing context-specific artifacts (e.g., lesson plans, reports, letters) under systematically varied persona gender, occupational role, and personality traits from the HEXACO and Dark Triad frameworks. Across 23,400 generated stories from six state-of-the-art LLMs, we find that personality traits are significantly associated with both the magnitude and direction of gender bias. In particular, Dark Triad personality traits are consistently associated with higher gender-stereotypical representations compared to socially desirable HEXACO traits, though these associations vary across models and languages. Our findings demonstrate that gender bias in LLMs is not static but context-dependent. This suggests that persona-conditioned systems used in real-world applications may introduce uneven representational harms, reinforcing gender stereotypes in generated educational, professional, or social content.
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