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This paper proposes a conversational framework that integrates correctness-by-design with generative AI and symbolic AI, using classical model checking to support users during Business Process Modeling. The BPMN-oriented instantiation extends beyond control-flow by enabling multi-party collaboration modeling and includes data flow. Evaluation shows 100% syntactic correctness and notion compliance, outperforming a BPMN-XML baseline under 30%, while the added usefulness of model checking needs further study.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-framework-for-llm-based-conceptual-modeling-application-to-bpmn-collaboration-diagrams/657464/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-framework-for-llm-based-conceptual-modeling-application-to-bpmn-collaboration-diagrams/657464.png","ImageObject",300,407,{"name":92,"@type":93},"The weather in my hometown","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-26",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address in LLM-based conceptual modeling?","Question",{"text":112,"@type":113},"LLMs can generate conceptual models with errors, either misrepresenting intended semantics or violating model properties. The paper aims to guide users toward correct representations by integrating generative and symbolic methods.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed framework ensure correctness during modeling?",{"text":117,"@type":113},"The framework combines conversational generation with classical model checking, adding correctness-by-design support so users can refine models while avoiding domain-independent errors.",{"name":119,"@type":110,"acceptedAnswer":120},"What is special about the BPMN Collaboration Diagrams instantiation?",{"text":121,"@type":113},"It goes beyond the control-flow perspective, enabling modeling of multi-party collaborations and including data flow, rather than only single-party process behavior.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},657464,1793115439,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1374408744247,"https://ap-avatar.wpscdn.com/avatar/14001251d37d565ec6f?x-image-process=image/resize,m_fixed,w_180,h_180&k=1790672120290115972","A Framework for LLM-Based Conceptual Modeling: Application to BPMN Collaboration Diagrams  \nAya Safan1 , Julius Köpke1, *  \n1 University of Klagenfurt, Department of Informatics Systems, Universitätsstraße 65 -67, 9020 Klagenfurt am Wörthersee, Austria [https://www.aau.at/en/isys/ics](https://www.aau.at/en/isys/ics)  \nAbstract  \nGenerative AI has shown promising capabilities in translating textual descriptions into conceptual models, opening new possibilities for supporting the modeling process. In the domain of Business Process Modeling, several studies and prototypes have demonstrated the general applicability of Large Language Models (LLMs) for generating process models from textual descriptions and user feedback. This paper presents a framework for the conversational modeling of conceptual models. Our framework integrates correctness by design, generative AI and symbolic AI in the form of classical model checking to assist the user in the modeling process. We instantiate this framework in the domain of Business Process Modeling. This instantiation is the first approach going beyond the control flow perspective, enabling the modeling of multi-party collaborations, including data flow. In our evaluation, the tool demonstrated 100% syntactic correctness and notion compliance of the generated models. A BPMN-XML baseline achieved this in less than 30% of the cases. In an additional expert user study, the tool received strong user support; however, the usefulness of the model-checking functionality requires further investigation.  \nKeywords  \nLarge Language Models, LLMs, Process Modeling, BPMN, Model Checking  \n1. Introduction  \nGenerative AI, particularly in the form of LLMs, has shown promising capabilities for translating textual descriptions into conceptual models [1] . These capabilities allow new modeling techniques, such as LLM-based conversational modeling. In this approach, a user first sends an initial description to a modeling system, which then generates and displays a corresponding conceptual model. In an interactive feedback loop, the user can provide feedback to correct potential issues and to extend or refine the model. We argue that this approach has the potential to reduce entry barriers and advance the productivity of the conceptual modeling process.  \nIn the most generic form, if an LLM-based modeling system could correctly interpret metamodeling languages such as MOF [2], it would be possible to use LLMs to generate arbitrary conceptual models from user input. However, as demonstrated in [3], current LLMs are not yet capable of performing this task reliably. While such a generic approach remains infeasible, several successful applications have emerged for specific output languages, such as ontologies [4] and Business Process Models [5, 6, 7, 8, 9] . Nevertheless, existing LLM-based conversational business process modeling prototypes are limited in scope. They typically focus on intra-organizational processes and address only the control flow perspective. Moreover, the generated models often depend on specially designed output formats that align with the current capabilities of LLMs.  \nWhile LLMs demonstrate notable strengths in processing natural language inputs, they are prone to producing modeling errors. Therefore, combining generative AI with symbolic AI represents a promising approach for developing real-world systems [10] . Models generated by LLMs often exhibit  \ntwo main types of deficits: they may misrepresent the user’s intended semantics, and they may include  \nER2025: Companion Proceedings of the 44th International Conference on Conceptual Modeling: Industrial Track, ER Forum, 8th SCME, Doctoral Consortium, Tutorials, Project Exhibitions, Posters and Demos, October 20 -23, 2025, Poitiers, France  \n* Corresponding author.  \n$ [aya.safan@aau.at](aya.safan@aau.at) (A. Safan); [julius.koepke@aau.at](julius.koepke@aau.at) (J. Köpke)  \n􀀚 0000-0002-6678-5731 (J. Köpke)  \n © 2025 Copyright for this paper","cbCaiuD5x2ofzSE1","https://ap.wps.com/l/cbCaiuD5x2ofzSE1","pdf",2111488,14,"English","# Abstract\n# 1. Introduction\n# 2. Framework for conversational conceptual modeling\n## 2.1. Usage scenario and requirements\n## 2.2. [Further framework sections]\n# 3. BPMN Collaboration Diagrams\n# 4. Implementation as a modeling tool\n# 5. Evaluation\n## 5.1. Syntactic correctness and notation compliance\n## 5.2. Expert user study\n# 6. Related work\n# 7. Conclusion","[{\"question\":\"What problem does the paper address in LLM-based conceptual modeling?\",\"answer\":\"LLMs can generate conceptual models with errors, either misrepresenting intended semantics or violating model properties. The paper aims to guide users toward correct representations by integrating generative and symbolic methods.\"},{\"question\":\"How does the proposed framework ensure correctness during modeling?\",\"answer\":\"The framework combines conversational generation with classical model checking, adding correctness-by-design support so users can refine models while avoiding domain-independent errors.\"},{\"question\":\"What is special about the BPMN Collaboration Diagrams instantiation?\",\"answer\":\"It goes beyond the control-flow perspective, enabling modeling of multi-party collaborations and including data flow, rather than only single-party process behavior.\"}]","A Framework for LLM-Based Conceptual Modeling: Application to BPMN Collaboration Diagrams | PDF",1792980075,35]