Predictive Process Monitoring is a branch of process mining that aims to predict the outcome of an ongoing process. Recently, it leveraged machine-and-deep learning architectures. In this paper, we extend our prior LLM-based Predictive Process Monitoring framework, which was initially focused on total time prediction via prompting. The extension consists of comprehensively evaluating its generality, semantic leverage, and reasoning mechanisms, also across multiple Key Performance Indicators. Empirical evaluations conducted on three distinct event logs and across the Key Performance Indicators of Total Time and Activity Occurrence prediction indicate that, in data-scarce settings with only 100 traces, the LLM surpasses the benchmark methods. Furthermore, the experiments also show that the LLM exploits both its embodied prior knowledge and the internal correlations among training traces. Finally, we examine the reasoning strategies employed by the model, demonstrating that the LLM does not merely replicate existing predictive methods but performs higher-order reasoning to generate the predictions.

翻译：预测性过程监控是过程挖掘的一个分支，旨在预测正在进行的过程的结果。近年来，它利用了机器学习和深度学习架构。在本文中，我们扩展了先前基于LLM的预测性过程监控框架，该框架最初专注于通过提示进行总时间预测。扩展内容包括全面评估其泛化能力、语义利用和推理机制，并涵盖多个关键绩效指标。在三个不同的事件日志上，针对总时间和活动发生预测这两个关键绩效指标进行的实证评估表明，在仅有100条迹线的数据稀缺场景下，LLM超越了基准方法。此外，实验还表明，LLM同时利用了其内化的先验知识和训练迹线间的内部关联。最后，我们检验了模型所采用的推理策略，证明LLM并非简单地复制现有的预测方法，而是通过高阶推理来生成预测。