发表机构
University of Pennsylvania; Cornell University; World Bank Group; University of Oxford; New York University（宾夕法尼亚大学; 康奈尔大学; 世界银行集团; 牛津大学; 纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过跨国调查和八种LLM测试发现，模型在直接问答时反映当地性别观念，但在生成当地语言媒体时偏向男性，需采用生成格式与本地基线评估全球部署中的性别偏见。
AI 中文摘要
大型语言模型（LLMs）越来越多地被用于生成媒体内容，但其内容是否延续性别刻板印象尚不清楚：标准基准依赖于基于选择的形式而非长篇生成，且西方以外地区当地性别关联的调查基线十分稀缺。我们收集了来自美国、印度、肯尼亚和尼日利亚的695名受访者对22种职业和家庭角色的性别关联数据，并在两种情境下评估了八个LLM：直接提问和媒体生成。模型在直接提问下与调查关联一致，但在主要当地语言单元的媒体生成下显著更偏向男性，这与人类制作的媒体中记录的男性偏见一致。在美国以外，英语提示下的偏移要小得多且不显著，因此仅英语或不分国家的评估会遗漏这些模型最常部署语言中的这种偏见。指令提示在方向上减少了偏移，但以牺牲与调查关联的一致性为代价。因此，评估LLM性别偏见以进行全球部署需要生成格式测试、当地语言提示和当地收集的人类基线。
英文摘要
Large language models (LLMs) are increasingly used to generate media, but whether their content perpetuates gender stereotypes is unknown: standard benchmarks rely on selection-based formats rather than long-form generation, and surveyed baselines for local gender associations are scarce outside the West. We collect gender associations for 22 occupational and domestic roles from 695 respondents across the United States, India, Kenya, and Nigeria, and evaluate eight LLMs under two regimes: direct questioning and media generation. Models track the surveyed associations under direct questioning but skew substantially more male under media generation in major local-language cells, consistent with the male bias documented in human-produced media. Outside the US, the shift is much smaller and non-significant under English prompting, so English-only or country-agnostic evaluation would miss this bias in the languages where these models are most deployed. Instruction prompting reduces the shift directionally, but trades off against alignment with the surveyed associations. Evaluating LLM gender bias for global deployment therefore requires generation-format testing, local-language prompting, and locally-collected human baselines.
