Forecasting future states of running process instances is one of the main challenges of Predictive Process Monitoring (PPM). Several deep learning approaches have recently achieved a valuable accuracy performance by addressing this task. On the other hand, with the recent boom of Large Language Models (LLMs) in multiple fields, LLMs have started attracting attention in PPM research also. In this study, we leverage the rich context of textual data to transform information recorded in event logs in smart textual data ready for boosting accurate PPM learning. In detail, we propose LUPIN, a LLM approach to predict the activity suffix of a running process instance. First it encodes historical running process instances in semantic text stories formulated according to narrative templates that account for information recorded in the event log. Then it fine tunes a pre-trained LLM model – medium BERT – on the text stories of historic running instances of a business process, to predict the activity suffix of any future running instance of the same business process. Finally, LUPIN integrates the XAI Integrated Gradient (IG) algorithm to explain how each part of the textual description of a running process instance has an effect on the prediction of its activity completion. The experimental evaluation explores the accuracy performance of LUPIN compared to that of several related methods and draws insights from the explanation retrieved through the IG algorithm.
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