A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects

2025-09-29

AI Summary

The rapid proliferation of open large language models (LLMs) has accelerated AI innovation, yet systematic understanding of their pre- and post-open-source collaboration mechanisms—critical for project initiation, governance, and ecosystem evolution—remains lacking. To address this gap, we conduct a longitudinal study of 14 open LLM projects and interview 28 global developers, applying qualitative thematic analysis. We propose, for the first time, five distinct collaborative organizational models for open LLMs, characterizing their divergent governance structures and community engagement mechanisms. We identify five key collaborative practices: shared data curation, co-developed evaluation benchmarks, framework interoperability, distributed compute resource sharing, and multilingual capability support. Empirical findings demonstrate their tangible impact on AI democratization, regional ecosystem development, and linguistic diversity. Our work provides actionable insights for policymakers and open-source practitioners to strengthen sustainable LLM collaboration.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Mapping collaboration practices in open LLM development lifecycle
• Analyzing diverse motivations behind open LLM project creation
• Identifying governance models across different open LLM organizations

Innovation

• Analyzed collaboration practices across 14 open LLM projects
• Identified diverse motivations including democratizing AI access
• Categorized five organizational models for governance strategies