There is growing interest in leveraging large language models (LLMs) for text-to-model translation and optimization tasks. This paper aims to advance this line of research by introducing \textsc{Text2Model} and \textsc{Text2Zinc}. \textsc{Text2Model} is a suite of co-pilots based on several LLM strategies with varying complexity, along with an online leaderboard. \textsc{Text2Zinc} is a cross-domain dataset for capturing optimization and satisfaction problems specified in natural language, along with an interactive editor with built-in AI assistant. While there is an emerging literature on using LLMs for translating combinatorial problems into formal models, our work is the first attempt to integrate \textit{both} satisfaction and optimization problems within a \textit{unified architecture} and \textit{dataset}. Moreover, our approach is \textit{solver-agnostic} unlike existing work that focuses on translation to a solver-specific model. To achieve this, we leverage \textsc{MiniZinc}'s solver-and-paradigm-agnostic modeling capabilities to formulate combinatorial problems. We conduct comprehensive experiments to compare execution and solution accuracy across several single- and multi-call strategies, including; zero-shot prompting, chain-of-thought reasoning, intermediate representations via knowledge-graphs, grammar-based syntax encoding, and agentic approaches that decompose the model into sequential sub-tasks. Our co-pilot strategies are competitive, and in parts improve, recent research in this domain. Our findings indicate that while LLMs are promising they are not yet a push-button technology for combinatorial modeling. We contribute \textsc{Text2Model} co-pilots and leaderboard, and \textsc{Text2Zinc} and interactive editor to open-source to support closing this performance gap.

翻译：利用大语言模型（LLMs）进行文本到模型翻译与优化任务的研究日益兴起。本文旨在推进这一研究方向，引入\textsc{Text2Model}与\textsc{Text2Zinc}。\textsc{Text2Model}是一套基于多种复杂度各异的大语言模型策略的协同驾驶系统，并配有在线排行榜。\textsc{Text2Zinc}是一个跨领域数据集，用于捕获自然语言中描述的优化与满足问题，同时提供内置AI助手的交互式编辑器。尽管目前已有关于使用大语言模型将组合问题翻译为形式化模型的前沿文献，但本文首次尝试将\textit{满足问题}与\textit{优化问题}整合进\textit{统一架构}和\textit{数据集}中。此外，现有研究侧重于翻译至特定求解器的模型，而我们的方法\textit{不依赖求解器}。为此，我们借助\textsc{MiniZinc}的求解器与范式无关建模能力来形式化组合问题。我们通过全面实验，比较了多种单次与多次调用策略的执行与求解精度，包括：零样本提示、思维链推理、基于知识图谱的中间表示、基于语法的语法编码，以及将模型分解为顺序子任务的智能体方法。我们的协同驾驶策略具有竞争力，并在部分领域改进了该领域近期研究。研究结果表明，尽管大语言模型前景广阔，但尚未成为组合建模的即用型技术。我们开源了\textsc{Text2Model}协同驾驶系统与排行榜，以及\textsc{Text2Zinc}和交互式编辑器，以助力缩小这一性能差距。