# Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?

arXiv: https://arxiv.org/abs/2304.11065 · HTML: https://mlsanity.com/paper/2304.11065/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 99 references that are in the map.
- First posted on arXiv 19 Apr 2023.

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

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing](https://mlsanity.com/paper/2004.14564/) (2020)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots](https://mlsanity.com/paper/2302.06466/) (2023)
- [Paraphrasing with Large Language Models](https://mlsanity.com/paper/1911.09661/) (2019)
- [A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques](https://mlsanity.com/paper/2104.08428/) (2021)
- [Just Tell Me: Prompt Engineering in Business Process Management](https://mlsanity.com/paper/2304.07183/) (2023)
- [PET: An Annotated Dataset for Process Extraction from Natural Language Text](https://mlsanity.com/paper/2203.04860/) (2022)
- [Response Generation with Context-Aware Prompt Learning](https://mlsanity.com/paper/2111.02643/) (2021)
- [A Model-based Chatbot Generation Approach to Converse with Open Data Sources](https://mlsanity.com/paper/2007.10503/) (2020)
- [Chatbot integration in few patterns](https://mlsanity.com/paper/2009.03101/) (2020)

## Built on by (most cited first, 4 of 4)

- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks](https://mlsanity.com/paper/2406.13264/) (2024)
- [BPMN Assistant: An LLM-Based Approach to Business Process Modeling](https://mlsanity.com/paper/2509.24592/) (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)

## Abstract (arXiv)

AI-driven chatbots such as ChatGPT have caused a tremendous hype lately. For BPM applications, several applications for AI-driven chatbots have been identified to be promising to generate business value, including explanation of process mining outcomes and preparation of input data. However, a systematic analysis of chatbots for their support of conversational process modeling as a process-oriented capability is missing. This work aims at closing this gap by providing a systematic analysis of existing chatbots. Application scenarios are identified along the process life cycle. Then a systematic literature review on conversational process modeling is performed, resulting in a taxonomy of application scenarios for conversational process modeling, including paraphrasing and improvement of process descriptions. In addition, this work suggests and applies an evaluation method for the output of AI-driven chatbots with respect to completeness and correctness of the process models. This method consists of a set of KPIs on a test set, a set of prompts for task and control flow extraction, as well as a survey with users. Based on the literature and the evaluation, recommendations for the usage (practical implications) and further development (research directions) of conversational process modeling are derived.