Domain modeling is typically an iterative process where the modeling expert interacts with the domain experts to complete and refine the model. Recently, we have seen several attempts trying to assist, or even replace, the modeler with a Large Language Model (LLM). Several prompting strategies have been attempted but with limited success so far.
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 ToT, we can decompose the modeling process into several substeps 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 to facilitate the formalization of the ToT process for domain modeling. Our approach is implemented as part of an open source tool availabe on GitHub.
