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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

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

classification cs.CY
keywords researchbigsciencemultilingualartifactslanguagelargemodelssocial
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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

    cs.SE 2026-04 conditional novelty 7.0

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

  2. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

    cs.CL 2022-11 unverdicted novelty 6.0

    BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.

  3. StarCoder: may the source be with you!

    cs.CL 2023-05 accept novelty 5.0

    StarCoderBase matches or beats OpenAI's code-cushman-001 on multi-language code benchmarks; the Python-fine-tuned StarCoder reaches 40% pass@1 on HumanEval while retaining other-language performance.