用大模型+精心设计提示词，自动把文字假设转成因果回路图。
Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques
- 通过定制化提示词引导大模型识别变量与因果关系。
- 在简单结构下生成的回路图质量接近专家水平。
- 适合初学者快速构建系统动力学模型，提升建模效率。
将动态假设转化为因果回路图（CLD）是系统动力学建模的关键步骤。从文本中提取关键变量与因果关系构建CLD对新手建模者而言既困难又耗时，制约了系统动力学工具的普及。本文提出并测试了一种利用大语言模型（LLMs）结合定制化提示技术，自动化实现动态假设到CLD转换的方法。首先阐述了LLM的工作机制及其构建CLD所需的推理能力，采用标准有向图结构。随后，基于权威系统动力学教材构建了若干简单动态假设及其对应的CLDs。对比四种不同的提示技术组合，评估其生成结果与专家标注的CLD的一致性。结果显示，在简单模型结构下，使用定制提示技术的LLM可生成质量与专家相当的CLD，显著加速建模过程。
原文摘要 · Abstract (English)
Transforming a dynamic hypothesis into a causal loop diagram (CLD) is crucial for System Dynamics Modelling. Extracting key variables and causal relationships from text to build a CLD is often challenging and time-consuming for novice modelers, limiting SD tool adoption. This paper introduces and tests a method for automating the translation of dynamic hypotheses into CLDs using large language models (LLMs) with curated prompting techniques. We first describe how LLMs work and how they can make the inferences needed to build CLDs using a standard digraph structure. Next, we develop a set of simple dynamic hypotheses and corresponding CLDs from leading SD textbooks. We then compare the four different combinations of prompting techniques, evaluating their performance against CLDs labeled by expert modelers. Results show that for simple model structures and using curated prompting techniques, LLMs can generate CLDs of a similar quality to expert-built ones, accelerating CLD creation.
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