To enhance language models' cultural awareness, we design a generalizable
pipeline to construct cultural knowledge bases from different online
communities on a massive scale. With the pipeline, we construct CultureBank, a
knowledge base built upon users' self-narratives with 12K cultural descriptors
sourced from TikTok and 11K from Reddit. Unlike previous cultural knowledge
resources, CultureBank contains diverse views on cultural descriptors to allow
flexible interpretation of cultural knowledge, and contextualized cultural
scenarios to help grounded evaluation. With CultureBank, we evaluate different
LLMs' cultural awareness, and identify areas for improvement. We also fine-tune
a language model on CultureBank: experiments show that it achieves better
performances on two downstream cultural tasks in a zero-shot setting. Finally,
we offer recommendations based on our findings for future culturally aware
language technologies. The project page is this https URL . The
code and model is at this https URL . The released
CultureBank dataset is at this https URL .

为增强语言模型的文化意识，我们设计了一个通用的流程来从不同的在线社区大规模构建文化知识库。通过该流程，我们构建了一个基于 TikTok 用户自述和 Reddit 的文化描述信息的 CultureBank 知识库，包含 12K 个文化描述符和 11K 个上下文化场景。使用 CultureBank，我们评估了不同语言模型的文化意识，并找出改进的领域。我们还在 CultureBank 上微调了一个语言模型：实验证明它在零 - shot 设定下在两个后续文化任务上有更好的性能。最后，基于我们的发现，我们为未来的文化意识语言技术提供了建议。