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. Cultural adaptation has applications across several creative industries and requires intimate knowledge of source and target cultures during translation. 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 benchmark different models for this task. We 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 including cultural biases and stereotypes. 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展现出卓越的翻译能力。翻译中一个常被忽视的方面是文化适应，即调整源文化指涉以适应目标文化。文化适应在多个创意产业中具有应用价值，且要求翻译过程中对源文化与目标文化具备深入认知。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内嵌着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，并构建了评估框架以比较不同模型在此任务上的表现。我们评估了现代大语言模型在文化适应中的性能，分析了其跨文化知识，同时关联了不同文化间的相关概念。我们还探讨了自动适应可能存在的问题，包括文化偏见与刻板印象。我们希望此项研究能增进对大语言模型文化理解能力及其在跨文化情境中创造性的认识。