In cross-cultural recipe adaptation, the goal is not only to ensure cultural appropriateness and retain the original dish's essence, but also to provide diverse options for various dietary needs and preferences. Retrieval Augmented Generation (RAG) is a promising approach, combining the retrieval of real recipes from the target cuisine for cultural adaptability with large language models (LLMs) for relevance. However, it remains unclear whether RAG can generate diverse adaptation results. Our analysis shows that RAG tends to overly rely on a limited portion of the context across generations, failing to produce diverse outputs even when provided with varied contextual inputs. This reveals a key limitation of RAG in creative tasks with multiple valid answers: it fails to leverage contextual diversity for generating varied responses. To address this issue, we propose CARRIAGE, a plug-and-play RAG framework for cross-cultural recipe adaptation that enhances diversity in both retrieval and context organization. To our knowledge, this is the first RAG framework that explicitly aims to generate highly diverse outputs to accommodate multiple user preferences. Our experiments show that CARRIAGE achieves Pareto efficiency in terms of diversity and quality of recipe adaptation compared to closed-book LLMs.

翻译：在跨文化食谱改编中，目标不仅是确保文化适宜性并保留原菜肴精髓，还需为不同饮食需求和偏好提供多样化选择。检索增强生成（RAG）是一种具有前景的方法，它通过结合检索目标菜系真实食谱实现文化适应性，并利用大型语言模型（LLMs）确保内容相关性。然而，RAG能否生成多样化的改编结果尚未明确。我们的分析表明，RAG在多次生成中倾向于过度依赖有限的上下文信息，即便提供多样化的语境输入，仍无法产生差异性输出。这揭示了RAG在存在多个正确答案的创造性任务中的关键缺陷：它未能利用上下文多样性生成差异化响应。为解决该问题，我们提出CARRIAGE——一种即插即用的跨文化食谱改编RAG框架，通过增强检索与上下文组织的多样性提升效果。据我们所知，这是首个明确以生成高度多样化输出为目标、适配多种用户偏好的RAG框架。实验表明，与闭卷式LLMs相比，CARRIAGE在食谱改编的多样性与质量之间实现了帕累托效率。