Application of the Tree-of-Thoughts Framework to LLM-Enabled Domain Modeling

Abstract

Domain modeling is typically an iterative process where modeling experts interact with domain experts to complete and refine the model. Recently, we have seen several attempts to assist, or even replace, the modeler with a Large Language Model (LLM). Several LLM prompting strategies have been attempted, but with limited success. In this paper, we advocate for the adoption of a Tree-of-Thoughts (ToT) strategy to overcome the limitations of current approaches based on simpler prompting strategies. With a ToT strategy, we can decompose the modeling process into several sub-steps using for each step a specialized set of generators and evaluators prompts to optimize the quality of the LLM output. As part of our adaptation, we provide a Domain-Specific Language (DSL) to facilitate the formalization of the ToT process for domain modeling. Our approach is implemented as part of an open source tool available on GitHub.