用大模型生成企业模型，效果稳定但复杂任务仍需专家把关。
Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities
- 基于知识图谱的企业建模，结合大模型生成
- 大模型生成结果变化小，但复杂任务可靠性下降
- 专家干预对保证模型准确性和完整性至关重要
大语言模型（LLMs）在企业建模中的角色正从学术研究转向工业应用，成为机器辅助生成企业模型的又一关键组件。本文采用基于知识图谱的企业建模方法，探究了大模型在此场景下的潜力。通过专家调查与基于ChatGPT-4o的实验发现，基于大模型的模型生成具有极小的变异性，但在复杂任务中可靠性明显下降。调查结果进一步表明，人类建模专家的监督与干预对于确保生成模型的准确性与完整性至关重要。
原文摘要 · Abstract (English)
The role of large language models (LLMs) in enterprise modeling has recently started to shift from academic research to that of industrial applications. Thereby, LLMs represent a further building block for the machine-supported generation of enterprise models. In this paper we employ a knowledge graph-based approach for enterprise modeling and investigate the potential benefits of LLMs in this context. In addition, the findings of an expert survey and ChatGPT-4o-based experiments demonstrate that LLM-based model generations exhibit minimal variability, yet remain constrained to specific tasks, with reliability declining for more intricate tasks. The survey results further suggest that the supervision and intervention of human modeling experts are essential to ensure the accuracy and integrity of the generated models.
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