A Cartography of Open Collaboration in Open Source AI: Mapping
  Practices, Motivations, and Governance in 14 Open Large Language Model
  Projects
Authors: name: Johan Linåker, name: Cailean Osborne, name: Jennifer Ding, name: Ben Burtenshaw

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.