性格特征会放大大模型在中英文叙事中的性别偏见。
Personality Shapes Gender Bias in Persona-Conditioned LLM Narratives Across English and Hindi: An Empirical Investigation
- 用六种主流大模型生成2.34万条故事，控制性别、职业和性格变量。
- 黑暗三联征性格使性别刻板印象更严重，而积极性格则缓解偏见。
- 研究揭示偏见动态可变，适合关注伦理与公平的AI开发者参考。
大型语言模型（LLMs）在教育、客服和社交平台等场景中广泛应用，常通过角色设定提升交互体验。然而，角色设定可能引发性格特征与性别偏见的相互作用。本文在英、印双语环境下开展受控实验，让六种前沿大模型生成23,400条故事，每篇展现印度职场人士在不同情境下（如教案、报告、信件）产出内容。实验系统性地改变角色性别、职业角色及性格特质（基于HEXACO与Dark Triad框架）。结果发现，性格特质显著影响性别偏见的程度与方向：具有黑暗三联征特质的角色更易产生性别刻板表达，而具备社会期望型HEXACO特质的角色则表现较中立。该效应在不同模型和语言间存在差异。研究证明，大模型中的性别偏见并非静态，而是依赖于上下文。这意味着，在真实应用中，角色化系统可能造成不均衡的代表性伤害，强化教育、职业或社交内容中的性别刻板印象。
原文摘要 · Abstract (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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。