论文标题
部分可观测时空混沌系统的无模型预测
Towards using Few-Shot Prompt Learning for Automating Model Completion
论文作者
论文摘要
我们提出了一种简单而新颖的方法，可以改善域建模活动的完成。我们的方法通过使用很少的弹奏及时学习来利用大型语言模型的力量，而无需训练或调整这些领域稀缺的大数据集的模型。我们实施了方法，并在完成静态和动态域图的完成后对其进行了测试。我们的初步评估表明，这种方法是有效的，并且可以在建模活动期间以不同的方式整合。
We propose a simple yet a novel approach to improve completion in domain modeling activities. Our approach exploits the power of large language models by using few-shot prompt learning without the need to train or fine-tune those models with large datasets that are scarce in this field. We implemented our approach and tested it on the completion of static and dynamic domain diagrams. Our initial evaluation shows that such an approach is effective and can be integrated in different ways during the modeling activities.
