No Language Data Left Behind: A Comparative Study of CJK Language Datasets in the Hugging Face Ecosystem

TL;DR

This study analyzes over 3,300 datasets in the Hugging Face ecosystem to understand how cultural, institutional, and research factors influence dataset creation and quality for Chinese, Japanese, and Korean NLP communities, aiming to improve resource sharing and LLM development.

Contribution

It provides a cross-linguistic comparison of CJK datasets, revealing distinct creation patterns and offering practical strategies for enhancing dataset quality and collaboration.

Findings

Chinese datasets are large-scale and institution-driven

Korean NLP datasets are grassroots and community-led

Japanese datasets focus on entertainment and subculture

Abstract

Recent advances in Natural Language Processing (NLP) have underscored the crucial role of high-quality datasets in building large language models (LLMs). However, while extensive resources and analyses exist for English, the landscape for East Asian languages - particularly Chinese, Japanese, and Korean (CJK) - remains fragmented and underexplored, despite these languages together serving over 1.6 billion speakers. To address this gap, we investigate the HuggingFace ecosystem from a cross-linguistic perspective, focusing on how cultural norms, research environments, and institutional practices shape dataset availability and quality. Drawing on more than 3,300 datasets, we employ quantitative and qualitative methods to examine how these factors drive distinct creation and curation patterns across Chinese, Japanese, and Korean NLP communities. Our findings highlight the large-scale and often institution-driven nature of Chinese datasets, grassroots community-led development in Korean NLP, and an entertainment- and subculture-focused emphasis on Japanese collections. By uncovering these patterns, we reveal practical strategies for enhancing dataset documentation, licensing clarity, and cross-lingual resource sharing - ultimately guiding more effective and culturally attuned LLM development in East Asia. We conclude by discussing best practices for future dataset curation and collaboration, aiming to strengthen resource development across all three languages.