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.

提出了一种使用大型语言模型将条件流程树 (CPT) 转换为业务流程文本概述 (BPTS) 的方法，通过拆分困难的 CPT 并逐个解决，实现了 93.42% 的正确率，比传统提示方法提高了 45.17%，解决了在无数据集情况下进行业务流程文档生成的问题，并有潜力为过程模型提取领域提供大量数据集。

大型语言模型在业务流程文本草图自动生成中的应用

Business Process Text Sketch Automation Generation Using Large Language  Model

Extracting workflow nets from textual descriptions can be used to simplify
guidelines or formalize textual descriptions of formal processes like business
processes and algorithms. The task of manually extracting processes, however,
requires domain expertise and effort. While automatic process model extraction
is desirable, annotating texts with formalized process models is expensive.
Therefore, there are only a few machine-learning-based extraction approaches.
Rule-based approaches, in turn, require domain specificity to work well and can
rarely distinguish relevant and irrelevant information in textual descriptions.
In this paper, we present GUIDO, a hybrid approach to the process model
extraction task that first, classifies sentences regarding their relevance to
the process model, using a BERT-based sentence classifier, and second, extracts
a process model from the sentences classified as relevant, using dependency
parsing. The presented approach achieves significantly better results than a
pure rule-based approach. GUIDO achieves an average behavioral similarity score
of $0.93$. Still, in comparison to purely machine-learning-based approaches,
the annotation costs stay low.

通过使用 BERT-based 句子分类器和依存句法分析，本文提出了一种名为 GUIDO 的混合方法来提取处理模型，取得了较纯粹基于规则的方法显著更好的结果，平均行为相似度得分为 0.93，并且相比于纯机器学习方法，标注成本低。

GUIDO: 从自然语言文本中发现和排序指南的混合方法

GUIDO: A Hybrid Approach to Guideline Discovery & Ordering from Natural  Language Texts

Process model extraction (PME) is a recently emerged interdiscipline between
natural language processing (NLP) and business process management (BPM), which
aims to extract process models from textual descriptions. Previous process
extractors heavily depend on manual features and ignore the potential relations
between clues of different text granularities. In this paper, we formalize the
PME task into the multi-grained text classification problem, and propose a
hierarchical neural network to effectively model and extract multi-grained
information without manually-defined procedural features. Under this structure,
we accordingly propose the coarse-to-fine (grained) learning mechanism,
training multi-grained tasks in coarse-to-fine grained order to share the
high-level knowledge for the low-level tasks. To evaluate our approach, we
construct two multi-grained datasets from two different domains and conduct
extensive experiments from different dimensions. The experimental results
demonstrate that our approach outperforms the state-of-the-art methods with
statistical significance and further investigations demonstrate its
effectiveness.

该研究介绍了利用多粒度文本分类问题，提出了一种层次化神经网络，以从文本描述中高效地建模和提取多粒度信息，该方法基于过程模型提取，结合自然语言处理（NLP）和业务流程管理（BPM）并使用粗到细的学习机制。经实验证明，该方法优于现有的最先进方法。