BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model
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Abstract (arXiv)
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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Dec 2022.
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Citing papers per year: 2023: 3; 2024: 6; 2025: 2.
11 papers.
- 2023StarCoder: may the source be with you!cited by 799
- 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuningcited by 177
- 2023The ROOTS Search Tool: Data Transparency for LLMscited by 31
- 2024The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligencecited by 23
- 2024The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hubcited by 18
- 2025Bhaasha, Bhasa, Zaban: A Survey for Low-Resourced Languages in South Asia -- Current Stage and Challengescited by 6
- 2024Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donationscited by 4
- 2023Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Facecited by 3
- 2024Characterising Open Source Co-opetition in Company-hosted Open Source Software Projects: The Cases of PyTorch, TensorFlow, and Transformerscited by 3
- 2024Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfercited by 1
- 2025A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projectscited by 1
- 2025A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects29 Sep 2025
- 2025Bhaasha, Bhasa, Zaban: A Survey for Low-Resourced Languages in South Asia -- Current Stage and Challenges15 Sep 2025
- 2024Characterising Open Source Co-opetition in Company-hosted Open Source Software Projects: The Cases of PyTorch, TensorFlow, and Transformers23 Oct 2024
- 2024Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donations26 Sep 2024
- 2024Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer19 Aug 2024
- 2024The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub20 May 2024
- 2024The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence20 Mar 2024
- 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning9 Feb 2024
- 2023StarCoder: may the source be with you!9 May 2023
- 2023Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face28 Feb 2023
- 2023The ROOTS Search Tool: Data Transparency for LLMs27 Feb 2023