Cross-cultural competence in large language models (LLMs) requires the ability to identify Culture-Specific Items (CSIs) and to adapt them appropriately across cultural contexts. Progress in evaluating this capability has been constrained by the scarcity of high-quality CSI-annotated corpora with parallel cross-cultural sentence pairs. To address this limitation, we introduce XCR-Bench, a Cross(X)-Cultural Reasoning Benchmark consisting of 4.9k parallel sentences and 1,098 unique CSIs, spanning three distinct reasoning tasks with corresponding evaluation metrics. Our corpus integrates Newmark's CSI framework with Hall's Triad of Culture, enabling systematic analysis of cultural reasoning beyond surface-level artifacts and into semi-visible and invisible cultural elements such as social norms, beliefs, and values. Our findings show that state-of-the-art LLMs exhibit consistent weaknesses in identifying and adapting CSIs related to social etiquette and cultural reference. Additionally, we find evidence that LLMs encode regional and ethno-religious biases even within a single linguistic setting during cultural adaptation. We release our corpus and code to facilitate future research on cross-cultural NLP.

翻译：大语言模型（LLMs）的跨文化能力要求其能够识别文化特定项（CSIs），并在不同文化语境中进行恰当适配。由于缺乏高质量CSI标注且包含平行跨文化句对的语料库，评估该能力的进展一直受限。为应对这一局限，我们提出了XCR-Bench——一个跨文化推理基准，包含4.9k个平行句对和1,098个独特CSIs，涵盖三个不同的推理任务及相应的评估指标。本语料库将纽马克的CSI框架与霍尔的文化三元论相结合，使得对文化推理的系统分析能够超越表层文化产物，深入半显性与隐性的文化要素（如社会规范、信仰和价值观）。研究结果表明，当前最先进的大语言模型在识别和适配涉及社交礼仪与文化参照的CSIs时存在持续性的薄弱环节。此外，我们发现证据表明，大语言模型在文化适配过程中，即使在同一语言环境下也会编码地域性与民族宗教偏见。我们公开了本语料库与代码，以促进未来跨文化自然语言处理的研究。