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BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model

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arxiv 2212.04960 v1 pith:NZWRGCG5 submitted 2022-12-09 cs.CY

classification cs.CY
keywords researchbigsciencemultilingualartifactslanguagelargemodelssocial
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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.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 8 citations worldwide. Full citation record

  1. From OSS to Open Source AI: an Exploratory Study of Collaborative Development Paradigm Divergence

    cs.SE 2026-04 conditional novelty 7.0 of 10

    Open source AI shows lower collaboration intensity, reduced direct contributions, and a shift toward adaptive use rather than joint improvement compared to traditional OSS.

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    A taxonomy of 98 openness concepts from across disciplines reveals that AI openness discussions overemphasize access, inspection, reuse, and organic behavior while underrepresenting fairness, diversity, autonomy, and ...

  3. Hidden Language Consistency Phenomena in Reasoning LLMs

    cs.CL 2026-08 conditional novelty 6.0 of 10

    Reasoning models often stop using the requested language as problems get harder, and this language breakdown can make accuracy look better than it is.

  4. A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects

    cs.SE 2025-09 conditional novelty 6.0 of 10

    A qualitative interview study of 14 open LLM projects maps where collaboration happens, why developers participate, and how projects are governed across the model lifecycle.

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