理解流程的大语言模型：面向语义感知流程挖掘的指令微调
LLMs that Understand Processes: Instruction-tuning for Semantics-Aware Process Mining
AI总结：
本文研究指令微调在语义感知流程挖掘中的应用，通过让大语言模型学习多任务提示-答案对提升泛化能力，实验表明其在流程发现和预测任务上显著提升性能，而异常检测任务效果因模型而异。
摘要：
流程挖掘越来越多地利用与事件相关联的文本信息来处理异常检测和流程发现等任务。这种语义感知的流程挖掘关注的是流程中哪些行为应当是可能的（即期望），从而为传统的、基于频率的技术提供了重要补充，后者关注的是已记录的行为（即现实）。大语言模型（LLMs）为处理语义感知任务提供了强大的手段。然而，迄今为止最佳性能是通过任务特定的微调实现的，这种方法计算密集，并且产生的模型只能处理某一特定任务。为了克服这种泛化能力的不足，我们在本文中研究了指令微调在语义感知流程挖掘中的潜力。这里指令微调的思想是让大语言模型接触不同任务的提示-答案对，例如异常检测和下一活动预测，使其更加熟悉流程挖掘，从而也能在未见过的任务（如流程发现）上表现更好。我们的研究结果表明指令微调的影响是多样的：虽然在流程发现和预测任务上性能显著提升，但在异常检测任务上不同模型的表现各异，这凸显了指令微调任务的选择对于实现预期结果至关重要。
英文摘要：
Process mining is increasingly using textual information associated with events to tackle tasks such as anomaly detection and process discovery. Such semantics-aware process mining focuses on what behavior should be possible in a process (i.e., expectations), thus providing an important complement to traditional, frequency-based techniques that focus on recorded behavior (i.e., reality). Large Language Models (LLMs) provide a powerful means for tackling semantics-aware tasks. However, the best performance is so far achieved through task-specific fine-tuning, which is computationally intensive and results in models that can only handle one specific task. To overcome this lack of generalization, we use this paper to investigate the potential of instruction-tuning for semantics-aware process mining. The idea of instruction-tuning here is to expose an LLM to prompt-answer pairs for different tasks, e.g., anomaly detection and next-activity prediction, making it more familiar with process mining, thus allowing it to also perform better at unseen tasks, such as process discovery. Our findings demonstrate a varied impact of instruction-tuning: while performance considerably improved on process discovery and prediction tasks, it varies across models on anomaly detection tasks, highlighting that the selection of tasks for instruction-tuning is critical to achieving desired outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
