MoRe: LLM-Based Domain Model Generation with Hybrid Self-Refinement

Generating domain models from requirements is a vital and complex challenge in automated software engineering. Although large language models (LLMs) have exhibited significant competence in this area, their propensity for hallucination frequently results in models that are redundant, inconsistent, or structurally unsound. To enhance the quality of automatically generated models, this paper introduces MoRe, an LLM-based approach to domain model generation with self-refinement. Within our approach, an LLM is first tasked with producing an initial domain model draft. Subsequently, a hybrid refinement—combining LLMs with a rule-based scanner—is employed to identify and correct common issues in the model. An empirical study was conducted using 30 domain modeling problems and four open-source LLMs. The results indicate that MoRe significantly improves the quality of generated domain models. This paper advocates for incorporating a self-refinement phase as a standard component in any automated modeling workflow.