用标准化决策图引导大模型，让复杂判断更可控可调
DMN-Guided Prompting: A Framework for Controlling LLM Behavior
- 将决策逻辑拆解为结构化组件，通过DMN图指导大模型推理路径
- 在课程作业反馈中表现优于传统思维链提示，学生认可度高
- 适合需精准控制生成逻辑的教育、审核等场景
大型语言模型在知识密集型流程中自动化决策逻辑方面展现出巨大潜力，但其效果高度依赖提示策略与质量。由于决策逻辑通常嵌入提示中，终端用户难以修改或优化。决策模型与表示法（DMN）提供了一种标准化的图形化方式，以结构化、用户友好的形式定义决策逻辑。本文提出一种基于DMN的提示框架，将复杂决策逻辑分解为更小、可管理的组件，引导大模型遵循结构化决策路径。我们在一门研究生课程中实现该框架，学生提交的作业及代表评分标准的DMN模型作为输入。授课教师评估生成的反馈并打标签用于性能评估。实验结果表明，该方法在案例研究中优于思维链（CoT）提示；学生在基于技术接受模型的调查中也报告了较高的感知有用性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown considerable potential in automating decision logic within knowledge-intensive processes. However, their effectiveness largely depends on the strategy and quality of prompting. Since decision logic is typically embedded in prompts, it becomes challenging for end users to modify or refine it. Decision Model and Notation (DMN) offers a standardized graphical approach for defining decision logic in a structured, user-friendly manner. This paper introduces a DMN-guided prompting framework that breaks down complex decision logic into smaller, manageable components, guiding LLMs through structured decision pathways. We implemented the framework in a graduate-level course where students submitted assignments. The assignments and DMN models representing feedback instructions served as inputs to our framework. The instructor evaluated the generated feedback and labeled it for performance assessment. Our approach demonstrated promising results, outperforming chain-of-thought (CoT) prompting in our case study. Students also responded positively to the generated feedback, reporting high levels of perceived usefulness in a survey based on the Technology Acceptance Model.
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