关键词: 大语言模型, 业务过程管理, 业务过程自动生成, 图神经网络

Abstract: The rapid development of large language models has a significant impact on business process management in the enterprise domain,leading to improved efficiency,cost reduction,enhanced customer experience,and fostering innovation.The automatic generation of business process in Business Process Management(BPM) is of great significance,allowing for business improvement through simulating and visualizing complex business process.To help improve processes and enhance efficiency,a method for automatic business process generation was proposed to integrate into practical business scenarios.The business process text was transformed into an abstract label sequence using a signal list.Then,a prompt template was constructed to obtain the adjacency table of abstract labels from the large language model,thereby determining the connection relationships between abstract labels and obtaining an initial graph,and the initial graph was inputted into a graph neural network with a transductive learning approach for supervised training.Finally,the direct temporal relationships between activities were predicted and transformed into a process diagram.Experiments showed that the proposed method achieved an overall F1-score of 0.67 in predicting temporal relationships between activities,which outperformed baseline methods and large language model methods in predicting successor,parallel and unrelated temporal relationships,and surpassed baseline methods in predicting selection relationship but fell behind large language model methods.

Key words: large language model, business process management, automatic generation of business process, graph neural network