标题： 探究LLMs超参数调优和提示工程在支持领域建模中的作用 显示英文标题

标题： Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling

摘要： 大规模语言模型（LLMs）的引入提高了软件工程任务中的自动化水平，包括在模型驱动工程（MDE）中的应用。 然而，使用通用的LLMs进行领域建模存在一定的局限性。 一种方法是采用微调模型，但这需要大量的计算资源，并可能导致灾难性遗忘等问题。 本文探讨了超参数调优和提示工程如何提高Llama 3.1模型在从文本描述生成领域模型方面的准确性。 我们使用基于搜索的方法对特定医疗数据模型的超参数进行调优，结果相比基线LLM有显著的质量提升。 随后，我们在十个不同的应用领域中测试优化后的超参数。 虽然这些解决方案并非普遍适用，但我们证明了将超参数调优与提示工程相结合可以在几乎所有检查的领域模型中提高结果。 显示英文摘要

摘要： 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.