# A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects arXiv: https://arxiv.org/abs/2509.25397 ยท HTML: https://mlsanity.com/paper/2509.25397/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 86 references that are in the map. - First posted on arXiv 29 Sep 2025. ## Builds on (most cited first, 28 of 28) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015) - [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019) - [Lessons from the Trenches on Reproducible Evaluation of Language Models](https://mlsanity.com/paper/2405.14782/) (2024) - [The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI](https://mlsanity.com/paper/2310.16787/) (2023) - [The Gradient of Generative AI Release: Methods and Considerations](https://mlsanity.com/paper/2302.04844/) (2023) - [Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models](https://mlsanity.com/paper/2405.05417/) (2024) - [Analyzing the Evolution and Maintenance of ML Models on Hugging Face](https://mlsanity.com/paper/2311.13380/) (2023) - [Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs](https://mlsanity.com/paper/2410.03730/) (2024) - [The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub](https://mlsanity.com/paper/2405.13058/) (2024) - [EleutherAI: Going Beyond "Open Science" to "Science in the Open"](https://mlsanity.com/paper/2210.06413/) (2022) - [Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face](https://mlsanity.com/paper/2508.06811/) (2025) - [BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model](https://mlsanity.com/paper/2212.04960/) (2022) - [The Future of Open Human Feedback](https://mlsanity.com/paper/2408.16961/) (2024) - [Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediaries](https://mlsanity.com/paper/2311.12573/) (2023) - [Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives](https://mlsanity.com/paper/2301.08488/) (2023) - [Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence](https://mlsanity.com/paper/2405.15802/) (2024) - [Public-private funding models in open source software development: A case study on scikit-learn](https://mlsanity.com/paper/2404.06484/) (2024) - [Systematic Literature Review of Commercial Participation in Open Source Software](https://mlsanity.com/paper/2405.16880/) (2024) - [Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donations](https://mlsanity.com/paper/2409.17876/) (2024) - [Characterising Open Source Co-opetition in Company-hosted Open Source Software Projects: The Cases of PyTorch, TensorFlow, and Transformers](https://mlsanity.com/paper/2410.18241/) (2024) - [Open Data Ecosystems -- an empirical investigation into an emerging industry collaboration concept](https://mlsanity.com/paper/2109.01378/) (2021) - [What to share, when, and where: balancing the objectives and complexities of open source software contributions](https://mlsanity.com/paper/2208.00047/) (2022) - [Sustaining Open Data as a Digital Common -- Design principles for Common Pool Resources applied to Open Data Ecosystems](https://mlsanity.com/paper/2208.01694/) (2022) - [Sustaining Maintenance Labor for Healthy Open Source Software Projects through Human Infrastructure: A Maintainer Perspective](https://mlsanity.com/paper/2408.06723/) (2024) ## Built on by (most cited first, 1 of 1) - [Selecting Language Models for Social Science: Start Small, Start Open, and Validate](https://mlsanity.com/paper/2601.10926/) (2026) ## Abstract (arXiv) The proliferation of open large language models (LLMs) is fostering a vibrant ecosystem in artificial intelligence (AI). However, the methods of collaboration used to develop open LLMs, both before and after their public release, have not yet been systematically studied, limiting our understanding of how open LLM projects are initiated, organised, and governed, as well as the opportunities to further foster this ecosystem. We address this gap through an exploratory analysis of open collaboration throughout the development and reuse lifecycle of open LLMs, drawing on semi-structured interviews with the developers of 14 diverse open LLM projects. These collaborations span multiple artefact domains -- including models, data, software, evaluation, compute, and community engagement -- each enabling distinct forms of participation and involving different stakeholders that evolves across the LLM development lifecycle, shifting from concentrated, selective engagement in the early stages to broader, distributed participation after model release. The open LLM developers are motivated by a variety of social, economic, and technological motivations, ranging from democratising access to AI and promoting open science to building regional ecosystems and expanding language representation. These dynamics are coordinated through a range of governance structures, typically formal and professionalised to varying degrees, including centralised company-led efforts to decentralised grassroots initiatives. We synthesise our findings in a conceptual model of open collaboration in open LLM ecosystems, provide recommendations for practice, and conclude that openness in open source AI is not a uniform property but an emergent outcome of how collaboration is organised across interconnected artefact domains, lifecycle stages, and institutional contexts.