人格特质对英语和印地语中角色条件化大语言模型叙事性别偏见的影响：一项实证研究

摘要

大型语言模型（LLMs）正日益广泛应用于教育、客服和社交平台等角色驱动型场景中，这些模型被设定在与用户互动时采用特定角色身份。虽然角色设定能提升用户体验和参与度，但同时也引发了关于人格特征如何与性别偏见及刻板印象相互作用的担忧。本研究通过受控实验，对英语和印地语中角色驱动的故事生成进行分析：每个故事描绘印度职场人士在系统性变化的角色性别、职业身份以及HEXACO和黑暗三联征人格框架下，生成特定场景的产出物（如教案、报告、信件）。基于六种前沿LLMs生成的23,400个故事发现，人格特质与性别偏见的程度和方向均存在显著关联。尤其值得注意的是，与社会期望型HEXACO特质相比，黑暗三联征人格特质始终与更高程度的性别刻板印象表征相关，尽管这些关联因模型和语言而异。我们的研究结果表明，LLMs中的性别偏见并非静态存在，而是具有情境依赖性。这意味着现实应用中的角色设定系统可能带来不平等的表征危害，在生成的教育、职业或社交内容中强化性别刻板印象。

English

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.