用行为一致性检测修复自然语言生成流程模型的歧义
Ambiguity Detection and Elimination in Automated Executable Process Modeling
- 通过关键绩效指标分布检测模型行为不一致，定位问题网关逻辑
- 在糖尿病肾病指南数据上使重生成模型的行为变异性显著降低
- 适合需要可靠自动化流程建模但无标准答案的医疗/政务场景
利用大语言模型从自然语言规范自动生成可执行的业务流程模型与表示（BPMN）正变得越来越普遍。然而，模糊或表述不全的文本可能生成结构合法但行为不同的模型。我们的目标并非证明某个生成的BPMN模型语义正确，而是检测自然语言规范在重复生成和仿真下是否无法支持稳定的可执行解释。我们提出一种诊断驱动的框架：通过关键绩效指标（KPI）的经验分布检测行为不一致，使用基于模型的诊断定位分歧至网关逻辑，将该逻辑映射回原始叙述段落，并通过基于证据的文本优化进行修复。在糖尿病肾病健康指导政策上的实验表明，该方法显著降低了重生成模型的行为变异性。最终形成一个闭环流程，可在缺乏真实BPMN模型的情况下验证并修复可执行流程规范。
原文摘要 · Abstract (English)
Automated generation of executable Business Process Model and Notation (BPMN) models from natural-language specifications is increasingly enabled by large language models. However, ambiguous or underspecified text can yield structurally valid models with different simulated behavior. Our goal is not to prove that one generated BPMN model is semantically correct, but to detect when a natural-language specification fails to support a stable executable interpretation under repeated generation and simulation. We present a diagnosis-driven framework that detects behavioral inconsistency from the empirical distribution of key performance indicators (KPIs), localizes divergence to gateway logic using model-based diagnosis, maps that logic back to verbatim narrative segments, and repairs the source text through evidence-based refinement. Experiments on diabetic nephropathy health-guidance policies show that the method reduces variability in regenerated model behavior. The result is a closed-loop approach for validating and repairing executable process specifications in the absence of ground-truth BPMN models.
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