When English Rewrites Local Knowledge: Global Narrative Dominance in Large Language Models 当英语重写地方知识：大型语言模型中的全球叙事主导

The most telling admission in this paper isn't in its findings; it's in its title. "Global Narrative Dominance." It’s a term that should be tattooed onto the forehead of every engineer in Silicon Valley and every researcher fine-tuning the next frontier model. This isn't a bug; it’s the foundational design principle of the entire large language model project.

The researchers, by meticulously crafting a Bengali cultural dataset—CulturalNB—have provided a forensic autopsy of a live patient. They've shown that when you ask an LLM a culturally specific question in English, you don't just get a translation; you get a hostile takeover. The model's default worldview, a silicon-based amalgamation of Western internet text and institutional knowledge, actively suppresses local context. It substitutes global narratives, like swapping a grandmother's hand-written recipe for a McDonald's nutritional chart. The question asked in Bengali elicits a more grounded, local answer. The same question posed in English triggers a "language-induced epistemic shift," forcing the model to don its colonial administrator's pith helmet.

Let's be blunt: this is not merely a "missing-knowledge" problem, as the authors astutely note. It’s an active prioritization problem. The model isn't just ignorant of Bengali traditions; it is aggressively substituting its own, more "authoritative" (i.e., globally dominant) ones. The institutional framing increases, the local perspectives get bulldozed. It’s the digital equivalent of replacing every local temple's idol with a standardized statue of a generic, multinational CEO and calling it "cultural synthesis."

The study's methodology is solid but reveals a deeper irony. The researchers had to build an entire parallel universe—English prompts, English evidence, English judges—to prove that the English-centric universe is the problem. It’s like needing to conduct a study in French to prove that French hegemony is marginalizing Breton. The very framework of evaluation is contaminated by the bias it seeks to measure. And the fact that nine state-of-the-art models exhibit this behavior so uniformly isn't surprising; it's damning. It suggests that cultural bias isn't a random flaw in one model's training data but a core output of the entire paradigm of scaling on the open internet's dominant languages.

The most potent finding is that even providing "local evidence" doesn't fully fix the language-induced warp. You can hand the model the perfect, culturally-anchored answer key, and the mere act of processing it through its English-tuned circuits still bleaches out some of the local hue. This implies the bias isn't just in the recall of facts, but in the very architecture of understanding. The model’s "thinking" happens in a space pre-sorted by English. Evidence doesn't just inform; it gets filtered, and the filter is set to "Western Worldview" by default.

So, what is CulturalNB, really? It’s a mirror held up to the monolingual, Anglophone assumptions baked into the AI industry. It’s a dataset that screams: your "universal" model is not universal. It’s a specific, culturally-bound entity masquerading as a neutral oracle. When we deploy these models as "cross-lingual knowledge interfaces," we are, in practice, deploying the most powerful cultural homogenization tool since the printing press. We’re not bridging knowledge divides; we’re paving over them with a single, globally-branded asphalt.

The real question this paper poses isn't technical; it’s political and philosophical. Who gets to define what is "knowledge"? If the interface defaults to the "global" (read: Western) narrative, even when discussing a Bengali festival or a local historical event, then we are automating epistemological colonialism. The model doesn't just answer questions; it reshapes the very context in which the question is valid.

This research is a vital alarm bell, but I fear it will be misheard as a call for better "diversity" in training data—a mere patch. The fix isn't just more Bengali text in the mix. It’s a fundamental re-engineering of how models handle and privilege cultural perspective. It might require entirely new architectures that don't treat English as the neutral substrate for all other thought. Until then, every time we use an LLM to explore another culture, we're not really exploring. We're just seeing our own reflection, distorted and imposed, staring back from a screen. And that reflection is getting monotonously, dangerously familiar.

这篇论文中最令人警醒的表述并非出自研究结论，而是其标题——“全球叙事主导权”。这个术语应当刺在硅谷每一位工程师以及调校下一代前沿模型的每一位研究者的额头上。这并非漏洞，而是整个大语言模型项目根本性的设计原则。

这篇论文中最发人深省的揭示不在实验结果，而在其标题：“全球叙事主导权”。这个词汇应当烙印在硅谷工程师与前沿模型研究者的思维中——它并非系统缺陷，而是整个大语言模型工程的基石性设计准则。

研究者通过精心构建孟加拉语文化数据集CulturalNB，对“活体样本”进行了一场解剖学式的深度剖析。他们证明：当用户用英语向大语言模型询问特定文化议题时，得到的不仅是翻译答案，更是一场话语权的暴力接管。模型内置的默认世界观——硅基架构上西方互联网文本与制度化知识的合成物——会主动压制在地语境。它用全球化叙事覆盖原生文化，好比用麦当劳营养表替换祖母手写的食谱。而用孟加拉语提问则能唤起更贴近现实的本土化应答。同一问题若用英语提出，则会触发“语言诱发的认知迁移”，迫使模型戴上殖民地官员的遮阳帽。

让我们直面本质：这绝非作者谨慎指出的“知识缺失”问题，而是系统性的主动价值覆写。模型不仅对孟加拉传统无知，更在激进地用自身那套更具“权威性”（即全球主导性）的叙事进行替换。制度化框架不断扩张，本土视角持续被碾平——这相当于将每座地方庙宇的神像替换为跨国企业CEO的标准化塑像，并美其名曰“文化融合”。

该研究方法论虽严谨，却揭露了更深的悖论：研究者必须构建一整套平行世界（英语提示词、英语证据链、英语评审团），才能证明英语中心主义宇宙本身存在问题。这就像必须用法语研究来证明法语霸权对布列塔尼语的边缘化。评估体系本身已被其试图测量的偏见所污染。而九个最先进模型展现出的共性，正揭示了这种偏见已内化为人工智能的基因序列。