Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends

• URL: http://arxiv.org/abs/2604.14034v1
• Date: Wed, 15 Apr 2026 16:15:03 GMT
• Title: Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends
• Abstract summary: This article presents a literature review of AI-driven methods for transforming natural language into BPMN process models.<n>The analysis reveals a shift from rule-based and traditional NLP pipelines toward LLM-based architectures.<n>The literature also exposes persistent challenges related to semantic correctness, evaluation fragmentation, and limited validation in real-world organizational contexts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have stimulated growing interest in automating or assisting Business Process Modeling tasks using natural language. Several approaches have been proposed to transform textual process descriptions into BPMN and related workflow models. However, the extent to which these approaches effectively support complex process modeling in organizational settings remains unclear. This article presents a literature review of AI-driven methods for transforming natural language into BPMN process models, with a particular focus on the role of LLMs. Following a structured review strategy, relevant studies were identified and analyzed to classify existing approaches, examine how LLMs are integrated into text-to-model pipelines, and investigate the evaluation practices used to assess generated models. The analysis reveals a clear shift from rule-based and traditional NLP pipelines toward LLM-based architectures that rely on prompt engineering, intermediate representations, and iterative refinement mechanisms. While these approaches significantly expand the capabilities of automated process model generation, the literature also exposes persistent challenges related to semantic correctness, evaluation fragmentation, reproducibility, and limited validation in real-world organizational contexts. Based on these findings, this review identifies key research gaps and discusses promising directions for future research, including the integration of contextual knowledge through Retrieval-Augmented Generation (RAG), its integration with LLMs, the development of interactive modeling architectures, and the need for more comprehensive and standardized evaluation frameworks.

Related papers

• Bridging the Agent-World Gap: Text World Models for LLM-based Agents [83.8237017249529]
  Large language model (LLM)-based agents are increasingly used in interactive textual environments.<n>Many remain largely reactive, mapping observations to actions without an explicit model of how these environments are structured and evolve.<n>This motivates text world models (TWMs): transition models over textual states that, given a state and a candidate action, predict the resulting webpage, terminal output, API response, or user reply.<n>We systematically review text world models for LLM-based agents, organized around a formal framework and the agent lifecycle.
  arXiv  Detail & Related papers  (2026-06-08T04:58:52Z)
• Pragmos: A Process Agentic Modeling System [0.0]
  Large Language Models (LLMs) are now being explored as tools to derive process models directly from textual descriptions.<n>We envision modeling as an open-ended conversational activity, best supported by an interactive, iterative process involving both humans and LLM.<n>We present our research agenda and introduce Pragmos, a prototype system that operationalizes this vision.
  arXiv  Detail & Related papers  (2026-04-30T01:49:19Z)
• Assessing the Business Process Modeling Competences of Large Language Models [40.495149980011924]
  Large language models (LLMs) have significantly expanded the possibilities for generating Business Process Model and Notation (BPMN) models directly from natural language.<n>We introduce BEF4LLM, a novel evaluation framework comprising four perspectives: syntactic quality, pragmatic quality, semantic quality, and validity.<n>Using BEF4LLM, we conduct a comprehensive analysis of open-source LLMs and benchmark their performance against human modeling experts.
  arXiv  Detail & Related papers  (2026-01-29T14:34:20Z)
• Discrete Diffusion in Large Language and Multimodal Models: A Survey [61.86669998363359]
  We provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs)<n>Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy.<n>We trace the historical development of dLLMs and dMLLMs, formalize the underlying mathematical frameworks, list commonly-used modeling methods, and categorize representative models.
  arXiv  Detail & Related papers  (2025-06-16T17:59:08Z)
• Continual Learning for Generative AI: From LLMs to MLLMs and Beyond [56.29231194002407]
  We present a comprehensive survey of continual learning methods for mainstream generative AI models.<n>We categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based.<n>We analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones.
  arXiv  Detail & Related papers  (2025-06-16T02:27:25Z)
• How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective [64.00022624183781]
  Large language models (LLMs) can assess relevance and support information retrieval (IR) tasks.<n>We investigate how different LLM modules contribute to relevance judgment through the lens of mechanistic interpretability.
  arXiv  Detail & Related papers  (2025-04-10T16:14:55Z)
• A Survey of Model Architectures in Information Retrieval [59.61734783818073]
  The period from 2019 to the present has represented one of the biggest paradigm shifts in information retrieval (IR) and natural language processing (NLP)<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude with a forward-looking discussion of emerging challenges and future directions.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• Leveraging Large Language Models for Enhanced Process Model Comprehension [33.803742664323856]
  In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the interpretability of complex process models.
  arXiv  Detail & Related papers  (2024-08-08T13:12:46Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Generative transformations and patterns in LLM-native approaches for software verification and falsification [1.4595796095047369]
  We argue that a foundational step towards a more disciplined engineering practice is a systematic understanding of the core functional units-generative transformations.<n>We first present a fine-grained taxonomy of generative transformations, abstracting prompt-based interactions into conceptual signatures.<n>Our analysis not only validates the utility of the taxonomy but also surfaces strategic gaps and cross-dimensional relationships.
  arXiv  Detail & Related papers  (2024-04-14T23:45:23Z)
• Process Modeling With Large Language Models [42.0652924091318]
  This paper explores the integration of Large Language Models (LLMs) into process modeling. We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models. Preliminary results demonstrate the framework's ability to streamline process modeling tasks.
  arXiv  Detail & Related papers  (2024-03-12T11:27:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.