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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.11065 |
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| _version_ | 1866910300105605120 |
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| author | Klievtsova, Nataliia Benzin, Janik-Vasily Kampik, Timotheus Mangler, Juergen Rinderle-Ma, Stefanie |
| author_facet | Klievtsova, Nataliia Benzin, Janik-Vasily Kampik, Timotheus Mangler, Juergen Rinderle-Ma, Stefanie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_11065 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models? Klievtsova, Nataliia Benzin, Janik-Vasily Kampik, Timotheus Mangler, Juergen Rinderle-Ma, Stefanie Computation and Language Artificial Intelligence 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. |
| title | Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2304.11065 |