University of the Basque Country and Cardiff University study: mainstream large models favor Japanese culture on 31,000 open-ended questions
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Researchers from the University of the Basque Country and Cardiff University tested mainstream large models with 31,000 open-ended cultural questions in 24 languages, finding that Gemini, ChatGPT, Claude, and Llama still disproportionately default to Japanese examples and paradigms after geographic restrictions are removed. The study locates the emergence point of the bias at the supervised fine-tuning (SFT) stage, arguing that the worldview at the pretraining stage is relatively balanced, while alignment training that pursues restraint and low conflict anchors Japanese-style cultural symbols into the models. The study also points out that the more aggressively a model is quantized and compressed, the more extreme this stereotypical preference becomes.
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The study locates the emergence point of cultural bias at the supervised fine-tuning stage, and points out that quantization compression amplifies this preference, offering a new perspective for alignment discussions.
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全网都在抨击大模型是美式价值观复读机，欧洲两所大学刚用 31000 个测试样本，直接打穿了这个固有认知。
大家都以为主流 AI 全是西方中心主义、张口闭口美英视角，但巴斯克大学和卡迪夫大学的研究员测完 31000 个开放问题后发现，几乎所有主流大模型在潜意识里都极度偏向日本文化，
也就是说，当剥离掉所有国家前缀去提问，几乎所有主流模型都默认把日本文化当成了全球通用模板。
实验剥离了所有地理限制，用 24 种语言只抛出开放性文化问题，比如某个地方的家庭观念是什么、日常吃什么食物。即使提问语言和东亚毫无关系，Gemini、ChatGPT、Claude、Llama 依然不成比例地默认输出日本的案例与范式。
最反常的转折还不是互联网语料污染，而是对齐阶段人工硬塞进去的副作用。 研究团队精准定位了偏见的出现节点，预训练阶段的模型世界观本来相当均衡，这种偏差是在监督微调 SFT 也就是教 AI 变得有用、礼貌、无害的那一刻，被生生焊进模型里的。
各大厂追求的克制、低冲突、极度礼貌的企业安全准则，在隐空间里被模型自动锚定成了日本式的文化符号，追求绝对安全的对齐训练，反倒成了最大的文化过滤器。
更棘手的是边缘量化，模型为了在端侧跑得更快被压缩得越狠，这种刻板偏好就被压缩得越极端，低精度推理直接把多元性碾碎成了单一标准答案。
我们天天讨论大模型会不会毁灭人类，现实却是人类亲手调出来的推理引擎，正在把全世界的普世常识悄悄置换成高度同质化的文化切片。