LLM-based Textualization from Illustrated Path Diagram

Abstract

Structural Equation Modeling (SEM) is widely used for causal analysis, but path diagrams generated by Structural Equation Modeling are difficult to understand for those who do not have knowledge about SEM. Therefore, by describing path diagrams as the result of Structural Equation Modeling in writing, the people who does not have relevant knowledge can obtain useful information from path diagrams. We propose a method to convert path diagrams into descriptive text based on Large Language Model.