Prior work has demonstrated large language models' (LLMs) potential to
discern statistical tendencies within their pre-training corpora. Despite that,
many examinations of LLMs' knowledge capacity focus on knowledge explicitly
appearing in the training data or implicitly inferable from similar contexts.
How well an LLM captures the corpus-level statistical trends of concepts for
reasoning, especially long-tail ones, is still underexplored. In this study, we
introduce a novel few-shot question-answering task (CPopQA) that examines LLMs'
statistical ranking abilities for long-tail cultural concepts (e.g., holidays),
with a specific focus on these concepts' popularity in the United States and
the United Kingdom, respectively. We curate a dataset containing 459 holidays
across 58 countries, generating a total of 6,000 QA testing pairs. Experiments
on four strong LLMs show that large models are capable of ranking long-tail
cultural concepts regarding their statistical tendency. Notably, GPT-3.5
displayed superior performance and exhibited its potential to identify
geo-cultural proximity across continents.

该研究通过引入一种新的少样本问答任务（CPopQA），评估了大型语言模型（LLMs）对长尾文化概念（如假期）的统计排名能力，特别关注这些概念在美国和英国的受欢迎程度，并发现 GPT-3.5 在跨大洲识别地理文化接近性方面表现出卓越性能。