Despite recent progress, large language models (LLMs) still face the challenge of appropriately reacting to the intricacies of social and cultural conventions. This paper presents MANGO, a methodology for distilling high-accuracy, high-recall assertions of cultural knowledge. We judiciously and iteratively prompt LLMs for this purpose from two entry points, concepts and cultures. Outputs are consolidated via clustering and generative summarization. Running the MANGO method with GPT-3.5 as underlying LLM yields 167K high-accuracy assertions for 30K concepts and 11K cultures, surpassing prior resources by a large margin in quality and size. In an extrinsic evaluation for intercultural dialogues, we explore augmenting dialogue systems with cultural knowledge assertions. Notably, despite LLMs inherently possessing cultural knowledge, we find that adding knowledge from MANGO improves the overall quality, specificity, and cultural sensitivity of dialogue responses, as judged by human annotators. Data and code are available for download.

翻译：尽管近期取得了进展，大型语言模型（LLMs）在恰当应对社会与文化习俗的复杂性方面仍面临挑战。本文提出MANGO方法，旨在提炼高精度、高召回率的文化知识断言。我们审慎地通过概念与文化两个切入点，迭代式地提示LLMs以达成此目标。输出结果通过聚类与生成式摘要进行整合。以GPT-3.5为基础LLM运行MANGO方法，针对3万个概念与1.1万种文化生成了16.7万条高精度断言，在质量与规模上均大幅超越现有资源。在跨文化对话的外部评估中，我们探索了用文化知识断言增强对话系统的方法。值得注意的是，尽管LLMs本身具备文化知识，但人类标注者的评估表明，加入MANGO提供的知识能显著提升对话回应的整体质量、具体性与文化敏感性。数据与代码已开放下载。