AI 中文总结
研究预训练语言模型供应链中生产者与消费者的错位问题，通过对50位Hugging Face生产者和95位GitHub消费者的双视角调查，发现双方在模型发现、文档实践、谱系追踪和模型治理采用上存在差异，为改进供应链相关方面提供了机会。
AI 中文摘要
预训练语言模型（PTLMs）在现代软件系统中越来越多地被用作依赖项，尽管先前的工作记录了 PTLM 供应链中存在的持续性结构问题，如发布实践不一致、元数据不完整以及 Hugging Face 和 GitHub 存储库之间的差异。此前未被探索的一个角度是，PTLM 生产者的发布实践与消费者的模型重用需求之间的错位可能解释了其中一些挑战，但这种错位背后的人类期望、流程和互动从未被研究过。因此，我们对 50 位 Hugging Face 生产者和 95 位 GitHub 消费者进行了首次双视角调查，以检查四个人工智能供应链维度上的生产者 - 消费者错位：模型发现、文档实践、谱系追踪和模型治理采用情况。我们发现，尽管生产者和消费者依赖相同的文档工件，但他们在关键元数据应记录在何处存在分歧。尽管 27.9%的生产者和 31.4%的消费者追踪直接父模型之外的谱系，但生产者主要为溯源和可重复性追踪模型谱系，而消费者主要受质量和可靠性问题驱动。此外，生产者认为简化模型发布的治理机制将产生最大的积极影响，而消费者则优先考虑文档和依赖透明度。这些发现凸显了改进 PTLM 供应链中模型文档规范、谱系可见性和治理支持的机会。
英文摘要
Pre-trained Language Models (PTLMs) are increasingly reused as dependencies in modern software systems, even though prior work has documented persistent structural problems in PTLM supply chains, such as inconsistent release practices, incomplete metadata, and divergence between Hugging Face and GitHub repositories. One previously unexplored angle on these problems is that misalignment between PTLM producers' release practices and consumers' model reuse needs may explain several of these challenges, yet the human expectations, processes and interactions underneath this misalignment have never been studied. As such, we conducted the first dual-perspective survey of 50 Hugging Face producers and 95 GitHub consumers to examine producer-consumer misalignments across four AI supply chain dimensions: model discovery, documentation practices, lineage tracing, and model governance adoption. We find that, although producers and consumers rely on the same documentation artifacts, they disagree on where critical metadata should be recorded. Even though 27.9% of producers and 31.4% of consumers trace lineage beyond the immediate parent model, producers primarily trace model lineage for provenance and reproducibility, whereas consumers are mainly driven by quality and reliability concerns. Furthermore, producers believe that governance mechanisms that streamline model release would have the greatest positive impact, whereas consumers prioritize documentation and dependency transparency. These findings highlight opportunities to improve model documentation conventions, lineage visibility, and governance support in PTLM supply chains.
