{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/conversational-process-modelling-state-of-the","title":"Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?","arxiv_id":"2304.11065","date":"2023-04-19","proceeding":null,"authors":["Nataliia Klievtsova","Janik-Vasily Benzin","Timotheus Kampik","Juergen Mangler","Stefanie Rinderle-Ma"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2304.11065v2","url_pdf":"https://arxiv.org/pdf/2304.11065v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"conversational-process-modelling-state-of-the","repo_url":"https://github.com/wsaccoun/convermod","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"systematic-literature-review","task_name":"Systematic Literature Review"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"graph-self-attention","method_name":"Graph Self-Attention"},{"method_slug":"hype","method_name":"HypE"},{"method_slug":"radam","method_name":"RAdam"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}