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This paper investigates the collaborative practices in open large language model (LLM) development by conducting semi-structured interviews with developers from 14 open LLM projects. It identifies that collaboration extends beyond the models themselves to include datasets, benchmarks, and compute partnerships, and that developers are driven by diverse motivations, including democratizing AI and promoting open science. The study also reveals five distinct organizational models employed by open LLM projects, varying in centralization and community engagement.
Open LLM development isn't just about the models themselves; it's a sprawling collaborative ecosystem encompassing datasets, benchmarks, and even compute partnerships, driven by a surprisingly diverse range of motivations from democratizing AI to fostering regional innovation.
The proliferation of open large language models (LLMs) is fostering a vibrant ecosystem of research and innovation 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 comprehensively studied, limiting our understanding of how open LLM projects are initiated, organized, and governed as well as what opportunities there are to foster this ecosystem even further. 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 open LLMs from grassroots projects, research institutes, startups, and Big Tech companies in North America, Europe, Africa, and Asia. We make three key contributions to research and practice. First, collaboration in open LLM projects extends far beyond the LLMs themselves, encompassing datasets, benchmarks, open source frameworks, leaderboards, knowledge sharing and discussion forums, and compute partnerships, among others. Second, open LLM developers have a variety of social, economic, and technological motivations, from democratizing AI access and promoting open science to building regional ecosystems and expanding language representation. Third, the sampled open LLM projects exhibit five distinct organizational models, ranging from single company projects to non-profit-sponsored grassroots projects, which vary in their centralization of control and community engagement strategies used throughout the open LLM lifecycle. We conclude with practical recommendations for stakeholders seeking to support the global community building a more open future for AI.