Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources
上市Deposited
Creator
McMillan-Major, Angelina
Alyafeai, Zaid
Biderman, Stella
Chen, Kimbo
De Toni, Francesco
Dupont, Gerard
Elsahar, Hady
Emezue, Chris
Fikri Aji, Alham
Ilic, Suzana
Khamis, Nurulaqilla
Leong, Colin
Masoud, Maraim
Soroa, Aitor
Suarez, Pedro Ortiz
Talat, Zeerak
van Strien, Daniel
()
Jernite, Yacine
添加到收藏
您无权访问任何现有集合。您可以创建一个新集合。
Abstract
In recent years, large-scale data collection efforts have prioritized the amount of data collected in order to improve the modeling capabilities of large language models. This prioritization, however, has resulted in concerns with respect to the rights of data subjects represented in data collections, particularly when considering the difficulty in interrogating these collections due to insufficient documentation and tools for analysis. Mindful of these pitfalls, we present our methodology for a documentation-first, human-centered data collection project as part of the BigScience initiative. We identified a geographically diverse set of target language groups (Arabic, Basque, Chinese, Catalan, English, French, Indic languages, Indonesian, Niger-Congo languages, Portuguese, Spanish, and Vietnamese, as well as programming languages) for which to collect metadata on potential data sources. To structure this effort, we developed our online catalogue as a supporting tool for gathering metadata through organized public hackathons. We present our development process; analyses of the resulting resource metadata, including distributions over languages, regions, and resource types; and our lessons learned in this endeavor.