Large language models are increasingly used to answer culturally grounded questions across languages, yet it remains unclear whether local cultural knowledge is better accessed through English or the local language. Existing evaluations face two key limitations: many rely on parallel template-based questions that may not reflect how cultural knowledge naturally appears, and raw accuracy conflates general language proficiency with language-conditioned knowledge access. We address these issues with a controlled framework built on real-world cultural questions collected from regional benchmarks and local sources. By crossing question type (culture-agnostic vs. culture-specific) with query language (English vs. local language), and estimating ability with a shared 1PL item response theory model, we separate proficiency from localized knowledge access. Across 13 locales and roughly 80 models, we find a consistent English advantage on culture-agnostic questions, indicating stronger English proficiency. However, after accounting for this proficiency gap, local languages show a positive knowledge-access advantage in nearly all locale-model settings. This advantage is often masked in raw accuracy but becomes more visible for frontier, regionally aligned, or language-adapted models. Our results suggest that weaker local-language performance does not necessarily imply weaker cultural knowledge; rather, local cultural knowledge may be more accessible through the local language but hidden by limited language proficiency.

翻译：大语言模型（LLMs）正日益被用于跨语言回答文化类问题，但本地文化知识究竟是通过英语还是本地语言更易获取，目前尚不明确。现有评估存在两大关键局限：其一，许多研究依赖基于平行模板的问题，这类问题难以反映文化知识的自然呈现方式；其二，原始准确率混淆了通用语言能力与语言条件性知识获取。为解决这些问题，我们构建了一个受控框架，基于从区域基准和本地资源中收集的真实文化问题展开研究。通过交叉问题类型（文化无关型 vs 文化特定型）与查询语言（英语 vs 本地语言），并利用共享的单参数项目反应理论（1PL IRT）模型估算能力，我们成功分离了语言熟练度与本地化知识获取能力。在13个地区约80个模型的实验中，我们发现文化无关型问题始终呈现英语优势，表明其语言熟练度更强。但在消除熟练度差异后，几乎所有地区-模型组合中，本地语言均展现出正向的知识获取优势。这种优势在原始准确率中往往被掩盖，但在前沿模型、区域适配模型或语言适配模型中更为显著。我们的结果表明：本地语言表现较弱并不必然意味着文化知识薄弱；相反，本地文化知识可能更易通过本地语言获取，只是被有限的语言熟练度所遮蔽。