# BPMN Assistant: An LLM-Based Approach to Business Process Modeling

arXiv: https://arxiv.org/abs/2509.24592 · HTML: https://mlsanity.com/paper/2509.24592/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [Process Modeling With Large Language Models](https://mlsanity.com/paper/2403.07541/) (2024)
- [Just Tell Me: Prompt Engineering in Business Process Management](https://mlsanity.com/paper/2304.07183/) (2023)
- [Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2407.02310/) (2024)
- [Evaluating Large Language Models on Business Process Modeling: Framework, Benchmark, and Self-Improvement Analysis](https://mlsanity.com/paper/2412.00023/) (2024)
- [ProMoAI: Process Modeling with Generative AI](https://mlsanity.com/paper/2403.04327/) (2024)
- [Leveraging Large Language Models (LLMs) for Process Mining (Technical Report)](https://mlsanity.com/paper/2307.12701/) (2023)
- [A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models](https://mlsanity.com/paper/2407.18540/) (2024)
- [Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges](https://mlsanity.com/paper/2110.03754/) (2021)
- [Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?](https://mlsanity.com/paper/2304.11065/) (2023)

## Built on by (most cited first, 2 of 2)

- [Beyond Control-Flow: Integrating the Resource Perspective into Multi-Collaborative Process Modeling from Text](https://mlsanity.com/paper/2605.24546/) (2026)
- [Artificial Intelligence and Modeling & Simulation: An Overview](https://mlsanity.com/paper/2608.00366/) (2026)

## Abstract (arXiv)

This paper presents BPMN Assistant, a tool that leverages Large Language Models for natural language-based creation and editing of BPMN diagrams. While direct XML generation is common, it is verbose, slow, and prone to syntax errors during complex modifications. We introduce a specialized JSON-based intermediate representation designed to facilitate atomic editing operations through function calling. We evaluate our approach against direct XML manipulation using a suite of state-of-the-art models, including GPT-5.1, Claude 4.5 Sonnet, and DeepSeek V3. Results demonstrate that the JSON-based approach significantly outperforms direct XML in editing tasks, achieving higher or equivalent success rates across all evaluated models. Furthermore, despite requiring more input context, our approach reduces generation latency by approximately 43% and output token count by over 75%, offering a more reliable and responsive solution for interactive process modeling.