多语言≠多文化：评估大语言模型的多语言能力与文化对齐之间的差距
Multilingual != Multicultural: Evaluating Gaps Between Multilingual Capabilities and Cultural Alignment in LLMs
AI总结：
该研究针对大语言模型存在的以美国为中心的文化偏差问题，提出将模型回复分布与世界价值观调查的多语言群体意见对比的方法，发现多语言能力与文化对齐无一致关联，自一致性更能预测多文化对齐，提示文化对齐需专门投入。
摘要：
大语言模型（LLM）在全球各类语言上的能力正不断提升。然而，跨语言交流的能力并不一定能转化为恰当的文化表征。一个核心问题是以美国为中心的偏差，即LLM反映的是美国文化价值观而非当地文化价值观。我们提出了一种新颖的方法，将LLM生成的回复分布与来自世界价值观调查（World Value Survey）的四种语言（丹麦语、荷兰语、英语和葡萄牙语）的群体层面意见数据进行对比。我们采用严谨的线性混合效应回归框架，比较了两类模型：谷歌的Gemma模型（20亿至270亿参数）以及OpenAI的turbo系列的连续迭代版本。在这两类模型中，我们未发现语言能力与文化对齐之间存在一致的关联。Gemma模型在不同语言上的语言能力与文化对齐呈正相关，而OpenAI模型则不存在这种关联。重要的是，我们发现自一致性比多语言能力更能预测多文化对齐效果。我们的研究结果表明，实现有意义的文化对齐需要在提升通用语言能力之外付出专门努力。
英文摘要：
Large Language Models (LLMs) are becoming increasingly capable across global languages. However, the ability to communicate across languages does not necessarily translate to appropriate cultural representations. A key concern is US-centric bias, where LLMs reflect US rather than local cultural values. We propose a novel methodology that compares LLM-generated response distributions against population-level opinion data from the World Value Survey across four languages (Danish, Dutch, English, and Portuguese). Using a rigorous linear mixed-effects regression framework, we compare two families of models: Google's Gemma models (2B--27B parameters) and successive iterations of OpenAI's turbo-series. Across the families of models, we find no consistent relationships between language capabilities and cultural alignment. While the Gemma models have a positive correlation between language capability and cultural alignment across languages, the OpenAI models do not. Importantly, we find that self-consistency is a stronger predictor of multicultural alignment than multilingual capabilities. Our results demonstrate that achieving meaningful cultural alignment requires dedicated effort beyond improving general language capabilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
