Purdah与父权制：评估并缓解开放式多语种大语言模型生成内容中的南亚偏见
Purdah and Patriarchy: Evaluating and Mitigating South Asian Biases in Open-Ended Multilingual LLM Generations
AI总结：
本研究针对南亚多语种LLM评估中文化交叉偏见被忽略的问题，构建涵盖多维度的文化偏见词典，分析10种南亚语言生成内容中的父权制相关偏见，并验证两种自我去偏见策略的效果，拓展了文化偏见研究的视角与范围。
摘要：
大语言模型（LLM）的评估工作往往忽略交叉性与文化特定偏见，在南亚这类代表性不足的多语种地区尤为突出。本研究针对这些空白，对10种印度-雅利安语与达罗毗荼语的LLM输出开展多语种交叉性分析，明确受purdah（深闺制度）与父权制影响的文化污名如何在生成任务中被强化。我们构建了一个基于文化背景的偏见词典，涵盖此前未被探究的交叉维度，包括性别、宗教、婚姻状况与子女数量。我们使用该词典量化开放式生成场景（如讲故事、爱好列举与待办清单）中的交叉偏见，以及自我去偏见的效果——这类场景下偏见表现隐蔽，在多语种语境中大多未被研究。最后，我们评估了两种自我去偏见策略（简单提示与复杂提示），衡量其在印度-雅利安语与达罗毗荼语中减少文化特定偏见的效果。本研究通过提出新型偏见词典与评估框架，为文化偏见研究提供了细致视角，突破了欧洲中心或小规模多语种研究的局限。
英文摘要：
Evaluations of Large Language Models (LLMs) often overlook intersectional and culturally specific biases, particularly in underrepresented multilingual regions like South Asia. This work addresses these gaps by conducting a multilingual and intersectional analysis of LLM outputs across 10 Indo-Aryan and Dravidian languages, identifying how cultural stigmas influenced by purdah and patriarchy are reinforced in generative tasks. We construct a culturally grounded bias lexicon capturing previously unexplored intersectional dimensions including gender, religion, marital status, and number of children. We use our lexicon to quantify intersectional bias and the effectiveness of self-debiasing in open-ended generations (e.g., storytelling, hobbies, and to-do lists), where bias manifests subtly and remains largely unexamined in multilingual contexts. Finally, we evaluate two self-debiasing strategies (simple and complex prompts) to measure their effectiveness in reducing culturally specific bias in Indo-Aryan and Dravidian languages. Our approach offers a nuanced lens into cultural bias by introducing a novel bias lexicon and evaluation framework that extends beyond Eurocentric or small-scale multilingual settings.
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