Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs
This work addresses predictive process monitoring for small-scale event logs, but it is incremental as it builds on prior LLM-based methods.
The paper tackles predictive process monitoring by extending an LLM-based framework to evaluate its performance across multiple key performance indicators, showing that in data-scarce settings with only 100 traces, the LLM surpasses benchmark methods.
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.