{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:30:15Z","timestamp":1777894215229,"version":"3.51.4"},"publisher-location":"Cham","reference-count":9,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031822247","type":"print"},{"value":"9783031822254","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":86,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Large language models (LLMs) are capable of efficiently understanding natural language by processing large volumes of text data. Natural language is also used in process descriptions, thus LLMs appear to be a suitable candidate to significantly improve business process modeling. Although plenty of third-party LLMs exist, they raise the risk of privacy disclosure, untrustworthiness, and generalizability of the results. This paper proposes a pipeline to use a local and fine-tuned LLM that expects a textual process description as input and finally generates a visual process tree representation. We instantiate our pipeline with Llama3 8B and fine-tune the LLM with a training set of 120 self-generated examples. Initial evaluation results of our LLM-based approach for automated business process modeling promise usefulness of the approach in terms of process model quality while preserving data privacy.<\/jats:p>","DOI":"10.1007\/978-3-031-82225-4_44","type":"book-chapter","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T03:01:17Z","timestamp":1743303677000},"page":"605-609","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Local Large Language Models for\u00a0Business Process Modeling"],"prefix":"10.1007","author":[{"given":"Kaan","family":"Apaydin","sequence":"first","affiliation":[]},{"given":"Yorck","family":"Zisgen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Bellan, P., Dragoni, M., Ghidini, C.: Extracting business process entities and relations from text using pre-trained language models and in-context learning. In: Enterprise Design, Operations, and Computing. Lecture Notes in Computer Science, vol. 13585, pp. 182\u2013199. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-17604-3_11"},{"key":"44_CR2","unstructured":"Bucher, M.J.J., Martini, M.: Fine-tuned \u2018small\u2019 LLMs (still) significantly outperform zero-shot generative AI models in text classification (2024)"},{"issue":"3","key":"44_CR3","first-page":"1","volume":"18","author":"HG Fill","year":"2023","unstructured":"Fill, H.G., Fettke, P., K\u00f6pke, J.: Conceptual modeling and large language models: impressions from first experiments With ChatGPT. Enterp. Modell. Inf. Syst. Archit. (EMISAJ) 18(3), 1\u201315 (2023)","journal-title":"Enterp. Modell. Inf. Syst. Archit. (EMISAJ)"},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Grohs, M., Abb, L., Elsayed, N., Rehse, J.R.: Large language models can accomplish business process management tasks. In: International Conference on Business Process Management, pp. 453\u2013465. Springer (2023)","DOI":"10.1007\/978-3-031-50974-2_34"},{"key":"44_CR5","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models (2021)"},{"key":"44_CR6","unstructured":"Jouck, T., Depaire, B.: PTandLogGenerator: a generator for artificial event data (2016)"},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Kourani, H., Berti, A., Schuster, D., van\u00a0der Aalst, W.M.P.: Process Modeling with large language models. In: International Conference on Business Process Modeling, Development and Support, pp. 229\u2013244. Springer (2024)","DOI":"10.1007\/978-3-031-61007-3_18"},{"issue":"11","key":"44_CR8","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/a13110279","volume":"13","author":"SJ van Zelst","year":"2020","unstructured":"van Zelst, S.J., Leemans, S.J.J.: Translating workflow nets to process trees: an algorithmic approach. Algorithms 13(11), 279 (2020)","journal-title":"Algorithms"},{"key":"44_CR9","unstructured":"Zhao, J., et al.: LoRA land: 310 fine-tuned LLMs that rival GPT-4, a technical report (2024)"}],"container-title":["Lecture Notes in Business Information Processing","Process Mining Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-82225-4_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T16:05:46Z","timestamp":1760889946000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-82225-4_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031822247","9783031822254"],"references-count":9,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-82225-4_44","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"28 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Process Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lyngby","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpm2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpmconference.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}