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 for future culturally aware language technologies. We release the CultureBank dataset, code and models at https://github.com/SALT-NLP/CultureBank. Our project page is at culturebank.github.io

Divergence of languages observed at the surface level is a major challenge encountered by multilingual data representation, especially when typologically distant languages are involved. Drawing inspiration from a formalist Chomskyan perspective towards language universals, Universal Grammar (UG), this article uses deductively pre-defined universals to analyze a multilingually heterogeneous phenomenon, event nominals. In this way, deeper universality of event nominals beneath their huge divergence in different languages is uncovered, which empowers us to break barriers between languages and thus extend insights from some synthetic languages to a non-inflectional language, Mandarin Chinese. Our empirical investigation also demonstrates this UG-inspired schema is effective: With its assistance, the inter-annotator agreement (IAA) for identifying event nominals in Mandarin grows from 88.02% to 94.99%, and automatic detection of event-reading nominalizations on the newly-established data achieves an accuracy of 94.76% and an F1 score of 91.3%, which significantly surpass those achieved on the pre-existing resource by 9.8% and 5.2%, respectively. Our systematic analysis also sheds light on nominal semantic role labeling. By providing a clear definition and classification on arguments of event nominal, the IAA of this task significantly increases from 90.46% to 98.04%.