LLMs能否制作牙买加 couscous？对 recipe generation 中文化新颖性的研究
Can LLMs Cook Jamaican Couscous? A Study of Cultural Novelty in Recipe Generation
AI总结：
本文研究LLMs在生成食谱时对文化新颖性的表现，发现其无法有效生成文化代表性的食谱，揭示了LLMs在文化导向生成中的局限性。
摘要：
大型语言模型（LLMs）越来越多地被用于生成和塑造文化内容，从叙事写作到艺术生产。尽管这些模型展示了出色的流畅性和生成能力，但先前的工作表明它们也表现出系统性的文化偏见，引发了关于刻板印象、同质化以及文化特定表达形式消失的担忧。理解LLMs是否能够有意义地与主导文化以外的多样化文化对齐仍然是一个关键挑战。在本文中，我们通过烹饪食谱的视角研究LLMs的文化适应性，这是一个文化、传统和创造力紧密交织的领域。我们基于GlobalFusion数据集，该数据集根据已建立的文化距离度量标准将人类来自不同国家的食谱配对。使用相同的国家对，我们用多个LLMs生成文化适应的食谱，从而在跨文化交流创作中进行人类与LLM行为的直接比较。我们的分析显示，LLMs未能生成具有文化代表性的改编。与人类不同，其生成的食谱的差异性与文化距离无关。我们进一步解释了这一差距。我们显示文化信息在内部模型表示中是弱保存的，模型在生成中通过误解创造力和传统的概念而夸大新颖性，并且它们无法识别适应与其相关国家以及在文化相关的元素如食材中进行基础。这些发现突显了当前LLMs在文化导向生成中的基本限制，并对其在文化敏感应用中的使用具有重要影响。
英文摘要：
Large Language Models (LLMs) are increasingly used to generate and shape cultural content, ranging from narrative writing to artistic production. While these models demonstrate impressive fluency and generative capacity, prior work has shown that they also exhibit systematic cultural biases, raising concerns about stereotyping, homogenization, and the erasure of culturally specific forms of expression. Understanding whether LLMs can meaningfully align with diverse cultures beyond the dominant ones remains a critical challenge. In this paper, we study cultural adaptation in LLMs through the lens of cooking recipes, a domain in which culture, tradition, and creativity are tightly intertwined. We build on the \textit{GlobalFusion} dataset, which pairs human recipes from different countries according to established measures of cultural distance. Using the same country pairs, we generate culturally adapted recipes with multiple LLMs, enabling a direct comparison between human and LLM behavior in cross-cultural content creation. Our analysis shows that LLMs fail to produce culturally representative adaptations. Unlike humans, the divergence of their generated recipes does not correlate with cultural distance. We further provide explanations for this gap. We show that cultural information is weakly preserved in internal model representations, that models inflate novelty in their production by misunderstanding notions such as creativity and tradition, and that they fail to identify adaptation with its associated countries and to ground it in culturally salient elements such as ingredients. These findings highlight fundamental limitations of current LLMs for culturally oriented generation and have important implications for their use in culturally sensitive applications.
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