揭示大模型在印地语生成中隐性性别偏见，比英语更严重。
'Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs
- 用受WinoBias启发的印地语数据集测试模型偏见
- 印地语模型偏见率达87.8%，远高于英语的33.4%
- 适合关注多语言AI公平性的研究者与开发者
大型语言模型（LLMs）在翻译、客服和教育等任务中广泛应用。尽管如此，这些模型在英语中已表现出显著性别偏见，而在相对欠代表的语言如印地语中，偏见更为突出。本研究探索印地语文本生成中的隐性性别偏见，并与英语进行对比。我们基于WinoBias设计了印地语数据集，用于评估GPT-4o和Claude-3 sonnet等模型的响应模式。结果显示，印地语生成中的性别偏见高达87.8%，远高于英语GPT-4o的33.4%。印地语输出频繁依赖职业、权力层级和社会阶层相关的性别刻板印象。该研究揭示了不同语言间性别偏见的差异，为生成式AI系统中的偏见治理提供了重要参考。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly being used to generate text across various languages, for tasks such as translation, customer support, and education. Despite these advancements, LLMs show notable gender biases in English, which become even more pronounced when generating content in relatively underrepresented languages like Hindi. This study explores implicit gender biases in Hindi text generation and compares them to those in English. We developed Hindi datasets inspired by WinoBias to examine stereotypical patterns in responses from models like GPT-4o and Claude-3 sonnet. Our results reveal a significant gender bias of 87.8% in Hindi, compared to 33.4% in English GPT-4o generation, with Hindi responses frequently relying on gender stereotypes related to occupations, power hierarchies, and social class. This research underscores the variation in gender biases across languages and provides considerations for navigating these biases in generative AI systems.
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