The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description. Specifically, the goal of the competition is to increase the accessibility and usability of optimization solvers by allowing non-experts to interface with them using natural language. We separate this challenging goal into two sub-tasks: (1) recognize and label the semantic entities that correspond to the components of the optimization problem; (2) generate a meaning representation (i.e., a logical form) of the problem from its detected problem entities. The first task aims to reduce ambiguity by detecting and tagging the entities of the optimization problems. The second task creates an intermediate representation of the linear programming (LP) problem that is converted into a format that can be used by commercial solvers. In this report, we present the LP word problem dataset and shared tasks for the NeurIPS 2022 competition. Furthermore, we investigate and compare the performance of the ChatGPT large language model against the winning solutions. Through this competition, we hope to bring interest towards the development of novel machine learning applications and datasets for optimization modeling.

翻译：自然语言优化（NL4Opt）竞赛旨在探索从文本描述中提取优化问题含义及形式化构建的方法。具体而言，该竞赛的目标是通过使非专家用户能够利用自然语言与求解器交互，提升优化求解器的可访问性与易用性。我们将这一具有挑战性的目标分解为两个子任务：（1）识别并标注与优化问题组件对应的语义实体；（2）根据检测到的问题实体生成该问题的语义表示（即逻辑形式）。第一个任务旨在通过检测并标记优化问题的实体来消除歧义；第二个任务则创建线性规划问题的中间表示，并将其转换为可被商业求解器使用的格式。本报告介绍了NeurIPS 2022竞赛的线性规划文字问题数据集与共享任务，同时探究并对比了ChatGPT大语言模型与优胜解决方案的性能表现。通过本次竞赛，我们期望激发学界对优化建模领域新型机器学习应用与数据集研发的兴趣。