  Business Process Management (BPM) is gaining increasing attention as it has
the potential to cut costs while boosting output and quality. Business process
document generation is a crucial stage in BPM. However, due to a shortage of
datasets, data-driven deep learning techniques struggle to deliver the expected
results. 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 get
the correct answer in one go, and it can find the pattern of transforming
simple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy,
the traditional prompts perform weakly and with low correctness. We suggest
using this technique to break down a difficult CPT into a number of basic CPTs
and then solve each one in turn, drawing inspiration from the
divide-and-conquer strategy. We chose 100 process trees with depths ranging
from 2 to 5 at random, as well as CPTs with many nodes, many degrees of
selection, and cyclic nesting. Experiments show that our method can achieve a
correct rate of 93.42%, which is 45.17% better than traditional prompting
methods. Our proposed method provides a solution for business process document
generation in the absence of datasets, and secondly, it becomes potentially
possible to provide a large number of datasets for the process model extraction
(PME) domain.
