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

Due to the recent rise in digital misinformation, there has been great interest shown in using LLMs for fact-checking and claim verification. In this paper, we answer the question: Do LLMs know multilingual facts and can they use this knowledge for effective fact-checking? To this end, we create a benchmark by filtering multilingual claims from the X-fact dataset and evaluating the multilingual fact-checking capabilities of five LLMs across five diverse languages: Spanish, Italian, Portuguese, Turkish, and Tamil on our benchmark. We employ three different prompting techniques: Zero-Shot, English Chain-of-Thought, and Cross-Lingual Prompting, using both greedy and self-consistency decoding. We extensively analyze our results and find that GPT-4o achieves the highest accuracy, but zero-shot prompting with self-consistency was the most effective overall. We also show that techniques like Chain-of-Thought and Cross-Lingual Prompting, which are designed to improve reasoning abilities, do not necessarily improve the fact-checking abilities of LLMs. Interestingly, we find a strong negative correlation between model accuracy and the amount of internet content for a given language. This suggests that LLMs are better at fact-checking from knowledge in low-resource languages. We hope that this study will encourage more work on multilingual fact-checking using LLMs.