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.

翻译：生成式人工智能的最新进展，特别是大型语言模型（LLMs），激发了利用自然语言自动化或辅助业务流程建模任务的日益增长的兴趣。已有多种方法被提出，用于将文本流程描述转化为BPMN及相关工作流模型。然而，这些方法在组织环境中有效支持复杂流程建模的程度仍不明确。本文对从自然语言到BPMN流程模型的AI驱动方法进行了文献综述，特别关注了LLMs的作用。遵循结构化的综述策略，识别并分析了相关研究，以对现有方法进行分类，考察LLMs如何被集成到文本到模型的流水线中，并探究用于评估生成模型的实践。分析揭示出从基于规则和传统NLP流水线向依赖提示工程、中间表示和迭代优化机制的LLM架构的明确转变。尽管这些方法显著扩展了自动化流程模型生成的能力，但文献也暴露了与语义正确性、评估碎片化、可重复性以及在现实组织环境中验证有限相关的持续挑战。基于这些发现，本综述识别了关键的研究空白，并探讨了未来研究的有前景方向，包括通过检索增强生成（RAG）整合上下文知识及其与LLMs的集成、交互式建模架构的开发，以及构建更全面和标准化评估框架的需求。