The BigScience Workshop was a value-driven initiative that spanned one and half years of interdisciplinary research and culminated in the creation of ROOTS, a 1.6TB multilingual dataset that was used to train BLOOM, one of the largest multilingual language models to date. In addition to the technical outcomes and artifacts, the workshop fostered multidisciplinary collaborations around large models, datasets, and their analysis. This in turn led to a wide range of research publications spanning topics from ethics to law, data governance, modeling choices and distributed training. This paper focuses on the collaborative research aspects of BigScience and takes a step back to look at the challenges of large-scale participatory research, with respect to participant diversity and the tasks required to successfully carry out such a project. Our main goal is to share the lessons we learned from this experience, what we could have done better and what we did well. We show how the impact of such a social approach to scientific research goes well beyond the technical artifacts that were the basis of its inception.
%0 Journal Article
%1 Akiki2022-co
%A Akiki, Christopher
%A Pistilli, Giada
%A Mieskes, Margot
%A Gallé, Matthias
%A Wolf, Thomas
%A Ilić, Suzana
%A Jernite, Yacine
%D 2022
%I arXiv
%K topic_language
%T BigScience: A case study in the social construction of a multilingual large language model
%X The BigScience Workshop was a value-driven initiative that spanned one and half years of interdisciplinary research and culminated in the creation of ROOTS, a 1.6TB multilingual dataset that was used to train BLOOM, one of the largest multilingual language models to date. In addition to the technical outcomes and artifacts, the workshop fostered multidisciplinary collaborations around large models, datasets, and their analysis. This in turn led to a wide range of research publications spanning topics from ethics to law, data governance, modeling choices and distributed training. This paper focuses on the collaborative research aspects of BigScience and takes a step back to look at the challenges of large-scale participatory research, with respect to participant diversity and the tasks required to successfully carry out such a project. Our main goal is to share the lessons we learned from this experience, what we could have done better and what we did well. We show how the impact of such a social approach to scientific research goes well beyond the technical artifacts that were the basis of its inception.
@article{Akiki2022-co,
abstract = {The BigScience Workshop was a value-driven initiative that spanned one and half years of interdisciplinary research and culminated in the creation of ROOTS, a 1.6TB multilingual dataset that was used to train BLOOM, one of the largest multilingual language models to date. In addition to the technical outcomes and artifacts, the workshop fostered multidisciplinary collaborations around large models, datasets, and their analysis. This in turn led to a wide range of research publications spanning topics from ethics to law, data governance, modeling choices and distributed training. This paper focuses on the collaborative research aspects of BigScience and takes a step back to look at the challenges of large-scale participatory research, with respect to participant diversity and the tasks required to successfully carry out such a project. Our main goal is to share the lessons we learned from this experience, what we could have done better and what we did well. We show how the impact of such a social approach to scientific research goes well beyond the technical artifacts that were the basis of its inception.},
added-at = {2024-09-10T10:41:24.000+0200},
author = {Akiki, Christopher and Pistilli, Giada and Mieskes, Margot and Gall{\'e}, Matthias and Wolf, Thomas and Ili{\'c}, Suzana and Jernite, Yacine},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2e61daee836235598e245b5fc10e4bd6d/scadsfct},
interhash = {eb5a12698744aaf5431cf87fc45ad682},
intrahash = {e61daee836235598e245b5fc10e4bd6d},
keywords = {topic_language},
publisher = {arXiv},
timestamp = {2024-11-28T17:41:13.000+0100},
title = {{BigScience}: A case study in the social construction of a multilingual large language model},
year = 2022
}
