{"ID":2892898,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.14735","arxiv_id":"2507.14735","title":"Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling","abstract":"The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resources and can lead to issues like catastrophic forgetting. This paper explores how hyperparameter tuning and prompt engineering can improve the accuracy of the Llama 3.1 model for generating domain models from textual descriptions. We use search-based methods to tune hyperparameters for a specific medical data model, resulting in a notable quality improvement over the baseline LLM. We then test the optimized hyperparameters across ten diverse application domains. While the solutions were not universally applicable, we demonstrate that combining hyperparameter tuning with prompt engineering can enhance results across nearly all examined domain models.","short_abstract":"The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resour...","url_abs":"https://arxiv.org/abs/2507.14735","url_pdf":"https://arxiv.org/pdf/2507.14735v1","authors":"[\"Vladyslav Bulhakov\",\"Giordano d'Aloisio\",\"Claudio Di Sipio\",\"Antinisca Di Marco\",\"Davide Di Ruscio\"]","published":"2025-07-19T19:49:58Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}