mlsanityData to 7 Oct 2026

ProMoAI: Process Modeling with Generative AI

arXiv 2403.04327 ↗First posted 7 Mar 2024cs.DB cs.CLcited by 12

Not on the map. Why?

Abstract (arXiv)

ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, error handling, and code generation techniques. Beyond automating the generation of complex process models, ProMoAI also supports process model optimization. Users can interact with the tool by providing feedback on the generated model, which is then used for refining the process model. ProMoAI utilizes the capabilities LLMs to offer a novel, AI-driven approach to process modeling, significantly reducing the barrier to entry for users without deep technical knowledge in process modeling.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

14 of 32 references.

References · 14

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  3. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  4. 2023Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Modelscited by 172
  5. 2023ExpertPrompting: Instructing Large Language Models to be Distinguished Expertscited by 140
  6. 2023Generative AIcited by 119
  7. 2023Large Language Models can accomplish Business Process Management Taskscited by 34
  8. 2022Parameter-Efficient Sparsity for Large Language Models Fine-Tuningcited by 27
  9. 2023SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Modelscited by 26
  10. 2023Large Language Models for Business Process Management: Opportunities and Challengescited by 24
  11. 2023Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Studycited by 20
  12. 2023Just Tell Me: Prompt Engineering in Business Process Managementcited by 17
2 more
  1. 2022PET: An Annotated Dataset for Process Extraction from Natural Language Textcited by 15
  2. 2023A Composable Just-In-Time Programming Framework with LLMs and FBPcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 6.

12 papers.

  1. 2024PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Taskscited by 16
  2. 2024Leveraging Large Language Models for Enhanced Process Model Comprehensioncited by 5
  3. 2025Structured Extraction from Business Process Diagrams Using Vision-Language Modelscited by 3
  4. 2025BPMN Assistant: An LLM-Based Approach to Business Process Modelingcited by 2
  5. 2025What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Modelscited by 1
  6. 2025Knowledge-Driven Hallucination in Large Language Models: An Empirical Study on Process Modelingcited by 1
  7. 2026Assessing the Business Process Modeling Competences of Large Language Modelscited by 1
  8. 2026Automatic Generation of Executable BPMN Models from Medical Guidelinescited by 1
  9. 2026Beyond Control-Flow: Integrating the Resource Perspective into Multi-Collaborative Process Modeling from Textcited by 0
  10. 2026LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelinescited by 0
  11. 2026Design and Implementation of Agentic Orchestrations and Orchestration of Agentscited by 0
  12. 2026LLM-based Vulnerability Discovery in Business Process Documentationcited by 0