# Process Modeling With Large Language Models

arXiv: https://arxiv.org/abs/2403.07541 · HTML: https://mlsanity.com/paper/2403.07541/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2024.

## Built on by (most cited first, 26 of 26)

- [PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks](https://mlsanity.com/paper/2407.13244/) (2024)
- [Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2407.02310/) (2024)
- [MAO: A Framework for Process Model Generation with Multi-Agent Orchestration](https://mlsanity.com/paper/2408.01916/) (2024)
- [A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models](https://mlsanity.com/paper/2407.18540/) (2024)
- [Leveraging Large Language Models for Enhanced Process Model Comprehension](https://mlsanity.com/paper/2408.08892/) (2024)
- [LLM4PM: A case study on using Large Language Models for Process Modeling in Enterprise Organizations](https://mlsanity.com/paper/2407.17478/) (2024)
- [Bridging Domain Knowledge and Process Discovery Using Large Language Models](https://mlsanity.com/paper/2408.17316/) (2024)
- [NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods](https://mlsanity.com/paper/2409.13738/) (2024)
- [On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2504.21074/) (2025)
- [BPMN Assistant: An LLM-Based Approach to Business Process Modeling](https://mlsanity.com/paper/2509.24592/) (2025)
- [Instructional Text Across Disciplines: A Survey of Representations, Downstream Tasks, and Open Challenges Toward Capable AI Agents](https://mlsanity.com/paper/2410.18529/) (2024)
- [Evaluating the Process Modeling Abilities of Large Language Models -- Preliminary Foundations and Results](https://mlsanity.com/paper/2503.13520/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text](https://mlsanity.com/paper/2507.08362/) (2025)
- [What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Models](https://mlsanity.com/paper/2507.11356/) (2025)
- [From Legacy to Standard: LLM-Assisted Transformation of Cybersecurity Playbooks into CACAO Format](https://mlsanity.com/paper/2508.03342/) (2025)
- [Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture](https://mlsanity.com/paper/2509.09775/) (2025)
- [Knowledge-Driven Hallucination in Large Language Models: An Empirical Study on Process Modeling](https://mlsanity.com/paper/2509.15336/) (2025)
- [PMAx: An Agentic Framework for AI-Driven Process Mining](https://mlsanity.com/paper/2603.15351/) (2026)
- [Assisted Data Annotation for Business Process Information Extraction from Textual Documents](https://mlsanity.com/paper/2410.01356/) (2024)
- [No AI Without PI! Object-Centric Process Mining as the Enabler for Generative, Predictive, and Prescriptive Artificial Intelligence](https://mlsanity.com/paper/2508.00116/) (2025)
- [Documenting SME Processes with Conversational AI: From Tacit Knowledge to BPMN](https://mlsanity.com/paper/2512.05122/) (2025)
- [Instruction-Tuning Open-Weight Language Models for BPMN Model Generation](https://mlsanity.com/paper/2512.12063/) (2025)
- [Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends](https://mlsanity.com/paper/2604.14034/) (2026)
- [Beyond Control-Flow: Integrating the Resource Perspective into Multi-Collaborative Process Modeling from Text](https://mlsanity.com/paper/2605.24546/) (2026)
- [A Hybrid LLM-Based Framework for Automated Security Annotation Generation in Business Process Models](https://mlsanity.com/paper/2608.14370/) (2026)

## Abstract (arXiv)

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 the accessibility of process modeling, offering a more intuitive entry point for non-experts while augmenting the efficiency of experts. 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.