Twenty-seven years ago, E. Freuder highlighted that "Constraint programming
represents one of the closest approaches computer science has yet made to the
Holy Grail of programming: the user states the problem, the computer solves
it". Nowadays, CP users have great modeling tools available (like Minizinc and
CPMpy), allowing them to formulate the problem and then let a solver do the
rest of the job, getting closer to the stated goal. However, this still
requires the CP user to know the formalism and respect it. Another significant
challenge lies in the expertise required to effectively model combinatorial
problems. All this limits the wider adoption of CP. In this position paper, we
investigate a possible approach to leverage pre-trained Large Language Models
to extract models from textual problem descriptions. More specifically, we take
inspiration from the Natural Language Processing for Optimization (NL4OPT)
challenge and present early results with a decomposition-based prompting
approach to GPT Models.

利用预训练的大型语言模型从文本问题描述中提取模型的可能方法，并通过基于分解的提示方法与 GPT 模型展示了早期结果。