English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Prior work has demonstrated large language models' (LLMs) potential todiscern statistical tendencies within their pre-training corpora. Despite that,many examinations of LLMs' knowledge capacity focus on knowledge explicitlyappearing in the training data or implicitly inferable from similar contexts.How well an LLM captures the corpus-level statistical trends of concepts forreasoning, especially long-tail ones, is still underexplored. In this study, weintroduce 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 andthe United Kingdom, respectively. We curate a dataset containing 459 holidaysacross 58 countries, generating a total of 6,000 QA testing pairs. Experimentson four strong LLMs show that large models are capable of ranking long-tailcultural concepts regarding their statistical tendency. Notably, GPT-3.5displayed superior performance and exhibited its potential to identifygeo-cultural proximity across continents.

CPopQA: Ranking Cultural Concept Popularity by LLMs