@inproceedings{li-etal-2025-llm-based-business,
    title = "{LLM}-based Business Process Models Generation from Textual Descriptions",
    author = "Li, Xiaoxuan  and
      Ni, Lin  and
      Wang, Xin  and
      Yitong, Tang  and
      Li, Ruoxuan  and
      Liu, Jiamou  and
      Wang, Zhongsheng",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.31/",
    pages = "523--533",
    ISBN = "979-8-89176-303-6",
    abstract = "Business process modeling has traditionally depended on manual efforts or rigid rule-based techniques, limiting scalability and flexibility. Recent progress in Large Language Models (LLMs) enables automatic generation of process models from text, yet a systematic evaluation remains lacking. This paper explores the ability of LLMs to produce structurally and semantically valid business process workflows using five approaches: zero-shot, zero-shot CoT, few-shot, few-shot CoT, and fine-tuning. We assess performance under increasing control-flow complexity (e.g., nested gateways, parallel branches) using the MaD dataset, and introduce a masked-input setting to test semantic robustness. Results show that while fine-tuning achieves the best accuracy, few-shot CoT excels in handling complex logic and incomplete inputs. These findings reveal the strengths and limits of LLMs in process modeling and offer practical guidance for enterprise Business Process Management (BPM) automation."
}