Building upon the considerable advances in Large Language Models (LLMs), we are now equipped to address more sophisticated tasks demanding a nuanced understanding of cross-cultural contexts. A key example is recipe adaptation, which goes beyond simple translation to include a grasp of ingredients, culinary techniques, and dietary preferences specific to a given culture. We introduce a new task involving the translation and cultural adaptation of recipes between Chinese and English-speaking cuisines. To support this investigation, we present CulturalRecipes, a unique dataset comprised of automatically paired recipes written in Mandarin Chinese and English. This dataset is further enriched with a human-written and curated test set. In this intricate task of cross-cultural recipe adaptation, we evaluate the performance of various methods, including GPT-4 and other LLMs, traditional machine translation, and information retrieval techniques. Our comprehensive analysis includes both automatic and human evaluation metrics. While GPT-4 exhibits impressive abilities in adapting Chinese recipes into English, it still lags behind human expertise when translating English recipes into Chinese. This underscores the multifaceted nature of cultural adaptations. We anticipate that these insights will significantly contribute to future research on culturally-aware language models and their practical application in culturally diverse contexts.

翻译：基于大型语言模型（LLMs）的显著进展，我们现在能够处理需要细致理解跨文化语境的更复杂任务。一个关键示例是菜谱改编，它超越了简单的翻译，需要掌握特定文化中的食材、烹饪技巧和饮食偏好。我们引入了一项新任务，涉及中英文菜谱之间的翻译和文化适应。为支持这一研究，我们提出了CulturalRecipes，一个由自动配对的普通话中文和英文菜谱组成的独特数据集。该数据集还补充了人工编写和策划的测试集。在这个复杂的跨文化菜谱改编任务中，我们评估了多种方法的性能，包括GPT-4和其他LLMs、传统机器翻译以及信息检索技术。我们的全面分析涵盖了自动评估和人工评估指标。尽管GPT-4在将中文菜谱转换为英文方面表现出令人印象深刻的能力，但在将英文菜谱翻译为中文时仍落后于人类专家。这凸显了文化适应的多面性。我们预计，这些见解将为未来关于文化感知语言模型及其在多元文化背景下的实际应用做出重要贡献。