用大模型预测小数据流程，效果超越传统方法。
Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs
- 基于提示工程的LLM框架，利用先验知识和日志内部关联
- 仅100条日志时，总时间和活动出现预测均优于基准方法
- 能进行高阶推理，非简单复制已有方法，适合小样本场景
预测性流程监控是流程挖掘的一个分支，旨在预测正在进行的流程结果。近年来，该领域引入了机器学习与深度学习架构。本文扩展了我们先前基于大语言模型（LLM）的预测性流程监控框架，该框架最初专注于通过提示工程实现总时间预测。新工作全面评估了其通用性、语义利用能力和推理机制，涵盖多个关键绩效指标（KPI）。在三个不同事件日志上，针对总时间和活动出现预测的实证评估表明，在仅有100条轨迹的数据稀缺场景下，LLM的表现优于基准方法。此外，实验还显示，LLM有效利用了其内置先验知识以及训练轨迹间的内部相关性。最后，我们分析了模型的推理策略，证明LLM并非简单复现现有预测方法，而是执行更高阶推理以生成预测。
原文摘要 · Abstract (English)
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.
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