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Business Process Management (BPM) is gaining increasing attention as it hasthe potential to cut costs while boosting output and quality. Business processdocument generation is a crucial stage in BPM. However, due to a shortage ofdatasets, data-driven deep learning techniques struggle to deliver the expectedresults. We propose an approach to transform Conditional Process Trees (CPTs)into Business Process Text Sketches (BPTSs) using Large Language Models (LLMs).The traditional prompting approach (Few-shot In-Context Learning) tries to getthe correct answer in one go, and it can find the pattern of transformingsimple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy,the traditional prompts perform weakly and with low correctness. We suggestusing this technique to break down a difficult CPT into a number of basic CPTsand then solve each one in turn, drawing inspiration from thedivide-and-conquer strategy. We chose 100 process trees with depths rangingfrom 2 to 5 at random, as well as CPTs with many nodes, many degrees ofselection, and cyclic nesting. Experiments show that our method can achieve acorrect rate of 93.42%, which is 45.17% better than traditional promptingmethods. Our proposed method provides a solution for business process documentgeneration in the absence of datasets, and secondly, it becomes potentiallypossible to provide a large number of datasets for the process model extraction(PME) domain.

Business Process Text Sketch Automation Generation Using Large Language Model