Year: 2026
Type: conference-paper
Abstract: Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and informing AI policy.However, geographic metadata is very rarely available, and countrylevel representation is often hidden behind broad language-level claims.We introduce At-lasNLP, a country-aware atlas of over 13,000 NLP dataset records across normalized NLP task categories, tracking both the populations represented and where datasets are produced.AtlasNLP includes AtlasNLP-Gold, a humancurated reference set, and AtlasNLP-Core, an ACL-derived large-scale collection.Using this resource, we show that (1) dataset coverage is highly uneven across countries and tasks;(2) dataset production and representation are geographically asymmetric; and (3) language coverage does not imply geographic representation.These findings reveal blind spots in current dataset documentation practices and motivate more explicit geographic metadata for country-aware NLP evaluation.We release At-lasNLP at the AtlasNLP project site.
Authors: Joan Nwatu, Tsedeniya Solomon Amare, Longju Bai, Bontu Fufa Balcha, Zayd Bashir, Angana Borah, Zara Burzo, Yubin Choi, Naihao Deng, Samika Gupta, Michel Faloughi, Claude Kwizera, Ziqiao Ma, Cynthia Yacel Fuertes Panizo, Ellie Seehorn, Hui Shen, Jiayi Tang, Zesen Zhao, Boyuan Zheng, Rada Mihalcea
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: bronze
Funder: University of Michigan
OpenAlex ID: W7204863376