Process Modeling With Large Language Models
CoRR(2024)
摘要
In the realm of Business Process Management (BPM), process modeling plays a
crucial role in translating complex process dynamics into comprehensible visual
representations, facilitating the understanding, analysis, improvement, and
automation of organizational processes. Traditional process modeling methods
often require extensive expertise and can be time-consuming. This paper
explores the integration of Large Language Models (LLMs) into process modeling
to enhance flexibility, efficiency, and accessibility of process modeling for
both expert and non-expert users. We propose a framework that leverages LLMs
for the automated generation and iterative refinement of process models
starting from textual descriptions. Our framework involves innovative prompting
strategies for effective LLM utilization, along with a secure model generation
protocol and an error-handling mechanism. Moreover, we instantiate a concrete
system extending our framework. This system provides robust quality guarantees
on the models generated and supports exporting them in standard modeling
notations, such as the Business Process Modeling Notation (BPMN) and Petri
nets. Preliminary results demonstrate the framework's ability to streamline
process modeling tasks, underscoring the transformative potential of generative
AI in the BPM field.
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