Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure meaningful insights into people's work. Such insights are essential for improving digital products in ways grounded in real-world user interactions. Prior research has applied deep learning models to cluster user actions into high-level activities, but these approaches are highly sensitive to noise and struggle to generalize across applications. To address this limitation, we introduce WorkflowView, a framework that uses large language models (LLMs) to abstract low-level action sequences into high-level activities. We establish the effectiveness and generality of our approach across three distinct, challenging sequential tasks and diverse domains: (a) zero-shot task description reconstruction from browser logs (achieving high semantic similarity, $μ_{sim} = 0.91$), (b) few-shot student dropout prediction using MOOC interaction logs (reaching weighted $F_1 = 0.90$ with only five few-shot examples), and (c) anonymized, privacy-preserving analysis of AI tool integration within document workflows in Microsoft Word. Our work demonstrates that LLM-based abstraction is a robust and efficient path forward for transforming low-level behavioral data into high-level, interpretable, and actionable insights. We also discuss practical considerations for deploying LLM-based inferences within logging infrastructures, including computational efficiency and user privacy.

翻译：顺序或带时间戳的交互日志提供了数字应用程序使用的客观记录，但其粒度和噪声常常掩盖了关于人们工作内容的有意义洞察。这些洞察对于以真实用户交互为基础改进数字产品至关重要。以往研究应用深度学习模型将用户动作聚类为高级活动，但此类方法对噪声高度敏感且难以跨应用泛化。为解决这一局限，我们提出WorkflowView框架，该框架利用大型语言模型（LLMs）将低层级动作序列抽象为高层级活动。我们在三个不同且具有挑战性的序列任务及多样化领域中验证了方法的有效性与普适性：（a）基于浏览器日志的零样本任务描述重构（实现高语义相似度，$μ_{sim} = 0.91$），（b）利用MOOC交互日志进行少样本学生辍学预测（仅用五个少样本示例即达到加权$F_1 = 0.90$），以及（c）针对Microsoft Word文档工作流中AI工具集成的匿名化隐私保护分析。我们的研究表明，基于LLM的抽象化是将低层级行为数据转化为高层级、可解释且可操作洞察的稳健高效路径。我们还讨论了在日志基础设施中部署LLM推理的实际考量，包括计算效率和用户隐私。