Hugging Face 上机器学习模型与数据集文档、供应链及许可挑战的实证分析
An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face
AI总结：
本文实证分析 Hugging Face 上 760,460 个模型与 175,000 个数据集的文档、供应链结构和许可现状，指出合规与安全挑战并提出改进方向。
摘要：
过去十年间，机器学习（ML）组件在软件系统中得到广泛采用。这一现象几乎出现在从自然语言处理到计算机视觉的各个领域。这些 ML 组件既包括相对简单的神经网络，也包括复杂且资源密集的大语言模型。然而，尽管采用如此广泛，人们对产生这些模型的供应链关系仍知之甚少，而这可能对合规与安全产生影响。在本研究中，我们对从流行模型共享网站 Hugging Face 提取的 760,460 个模型和 175,000 个数据集进行了广泛分析。首先，我们评估 Hugging Face 供应链中文档的现状，报告现实世界中的缺陷实例，并提供可操作的改进建议。接着，我们分析现有供应链的底层结构。最后，我们对照以往工作所报告的情况，考察当前许可格局，并讨论该领域带来的独特挑战。我们的结果推动了多个研究方向，包括需要更好地管理 ML 模型/数据集许可证、更好地支持模型文档，以及自动化的不一致性检查与验证。我们公开了研究基础设施和数据集，以促进未来研究。
英文摘要：
The last decade has seen widespread adoption of Machine Learning (ML) components in software systems. This has occurred in nearly every domain, from natural language processing to computer vision. These ML components range from relatively simple neural networks to complex and resource-intensive large language models. However, despite this widespread adoption, little is known about the supply chain relationships that produce these models, which can have implications for compliance and security. In this work, we conducted an extensive analysis of 760,460 models and 175,000 datasets extracted from the popular model-sharing site Hugging Face. First, we evaluate the current state of documentation in the Hugging Face supply chain, report real-world examples of shortcomings, and offer actionable suggestions for improvement. Next, we analyze the underlying structure of the existing supply chain. Finally, we explore the current licensing landscape against what was reported in previous work and discuss the unique challenges posed in this domain. Our results motivate multiple research avenues, including the need for better license management for ML models/datasets, better support for model documentation, and automated inconsistency checking and validation. We make our research infrastructure and dataset available to facilitate future research.
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