This article presents a framework for generating optimisation models using a pre-trained generative transformer. The framework involves specifying the features that the optimisation model should have and using a language model to generate an initial version of the model. The model is then tested and validated, and if it contains build errors, an automatic edition process is triggered. An experiment was performed using MiniZinc as the target language and two GPT-3.5 language models for generation and debugging. The results show that the use of language models for the generation of optimisation models is feasible, with some models satisfying the requested specifications, while others require further refinement. The study provides promising evidence for the use of language models in the modelling of optimisation problems and suggests avenues for future research.

翻译：本文提出了一种利用预训练生成式Transformer生成优化模型的框架。该框架通过指定优化模型应具备的特征，并借助语言模型生成初始版本模型。随后对模型进行测试与验证，若存在构建错误，则自动触发编辑流程。实验以MiniZinc为目标语言，采用两个GPT-3.5语言模型分别执行生成与调试任务。结果表明，利用语言模型生成优化模型具有可行性——部分模型满足指定需求，而另一些模型仍需进一步改进。该研究为语言模型在优化问题建模中的应用提供了有前景的证据，并指明了未来研究方向。