@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://aclanthology.org/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."
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%0 Conference Proceedings
%T LLM-based Business Process Models Generation from Textual Descriptions
%A Li, Xiaoxuan
%A Ni, Lin
%A Wang, Xin
%A Yitong, Tang
%A Li, Ruoxuan
%A Liu, Jiamou
%A Wang, Zhongsheng
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S 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
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F li-etal-2025-llm-based-business
%X 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.
%U https://aclanthology.org/2025.findings-ijcnlp.31/
%P 523-533
Markdown (Informal)
[LLM-based Business Process Models Generation from Textual Descriptions](https://aclanthology.org/2025.findings-ijcnlp.31/) (Li et al., Findings 2025)
ACL
- Xiaoxuan Li, Lin Ni, Xin Wang, Tang Yitong, Ruoxuan Li, Jiamou Liu, and Zhongsheng Wang. 2025. LLM-based Business Process Models Generation from Textual Descriptions. In 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, pages 523–533, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.