LLMs are increasingly being deployed for multilingual applications and have demonstrated impressive translation capabilities between several low and high-resource languages. An aspect of translation that often gets overlooked is that of cultural adaptation, or modifying source culture references to suit the target culture. While specialized translation models still outperform LLMs on the machine translation task when viewed from the lens of correctness, they are not sensitive to cultural differences often requiring manual correction. LLMs on the other hand have a rich reservoir of cultural knowledge embedded within its parameters that can be potentially exploited for such applications. In this paper, we define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs for cultural adaptation and analyze their cross-cultural knowledge while connecting related concepts across different cultures. We also analyze possible issues with automatic adaptation. We hope that this task will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.

翻译：大语言模型正越来越多地应用于多语言场景，并在多种低资源与高资源语言之间的翻译任务中展现出卓越能力。翻译中常被忽视的一个方面是文化适应，即调整源文化指涉以适应目标文化。尽管从准确性角度看，专业翻译模型在机器翻译任务上仍优于大语言模型，但它们对文化差异不敏感，常需人工修正。而大语言模型的参数中嵌入了丰富的文化知识储备，有望在此类应用中发挥作用。本文界定了文化适应任务，构建了评估框架以评测现代大语言模型的文化适应能力，并在关联不同文化中相关概念的同时分析其跨文化知识。我们还探讨了自动适应可能存在的问题。我们希望此项研究能深化对大语言模型文化理解及其在跨文化场景中创造力的认知。