The challenges associated with using pre-trained models (PTMs) have not been specifically investigated, which hampers their effective utilization. To address this knowledge gap, we collected and analyzed a dataset of 5,896 PTM-related questions on Stack Overflow. We first analyze the popularity and difficulty trends of PTM-related questions. We find that PTM-related questions are becoming more and more popular over time. However, it is noteworthy that PTM-related questions not only have a lower response rate but also exhibit a longer response time compared to many well-researched topics in software engineering. This observation emphasizes the significant difficulty and complexity associated with the practical application of PTMs. To delve into the specific challenges, we manually annotate 430 PTM-related questions, categorizing them into a hierarchical taxonomy of 42 codes (i.e., leaf nodes) and three categories. This taxonomy encompasses many PTM prominent challenges such as fine-tuning, output understanding, and prompt customization, which reflects the gaps between current techniques and practical needs. We discuss the implications of our study for PTM practitioners, vendors, and educators, and suggest possible directions and solutions for future research.

翻译：使用预训练模型（PTMs）所面临的挑战尚未得到专门研究，这阻碍了其有效利用。为填补这一知识空白，我们收集并分析了Stack Overflow上5,896个与PTM相关的问题数据集。首先分析了PTM相关问题的流行度与难度趋势，发现此类问题随时间推移日益普遍。值得注意的是，与软件工程领域许多已充分研究的主题相比，PTM相关问题不仅回复率较低，而且响应时间更长。这一观察结果突显了PTM实际应用中的显著困难与复杂性。为深入探究具体挑战，我们手动标注了430个PTM相关问题，将其分类为包含42个编码（即叶节点）和三大类别的分层分类体系。该分类体系涵盖微调、输出理解、提示定制等众多关键挑战，反映了当前技术与实际需求之间的差距。本文讨论了研究对PTM实践者、供应商及教育工作者的启示，并为未来研究提出了可能的方向与解决方案。