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. For extrinsic evaluation, we explore augmenting dialogue systems with cultural knowledge assertions. 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，用于蒸馏高准确率、高召回率的常识知识断言。我们通过概念与文化两个入口，谨慎且迭代地提示大型语言模型以达成此目标。输出结果通过聚类与生成式摘要进行整合。以GPT-3.5作为底层模型运行MANGO方法，可获得涵盖3万个概念与1.1万种文化的16.7万条高准确率断言，显著超越现有资源。在外在评估中，我们探索将文化知识断言注入对话系统。人类评估者认为，添加来自MANGO的知识提升了对话响应的整体质量、特异性与文化敏感性。相关数据与代码均可下载。