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

Abstract

Featured Application: The proposed system is designed for integration into enterprise Business Process Management (BPM) platforms, with the aim of assisting non-technical business analysts in drafting and modifying executable BPMN models using natural language. This potential to reduce the dependency on technical modeling experts could accelerate the digital transformation workflow. 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. Conformance checking evaluation confirms that generated models preserve executable semantics, with JSON achieving an average F1 score of 0.72 compared to 0.69 for XML, though frontier models like GPT-5.1 and Claude 4.5 Sonnet demonstrated superior precision with direct XML generation. 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.