NL4Opt竞赛：基于自然语言描述的公式化优化问题
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
摘要：
自然语言优化（NL 4 Opt）竞赛旨在研究基于文本描述提取优化问题的含义和公式的方法。具体来说，比赛的目标是通过允许非专家使用自然语言与优化求解器进行交互来提高优化求解器的可访问性和可用性。我们将这个具有挑战性的目标分为两个子任务：（1）识别和标记与优化问题的组件相对应的语义实体;（2）生成含义表示（即，逻辑形式）的问题。第一个任务旨在通过检测和标记优化问题的实体来减少歧义。第二个任务创建线性规划（LP）问题的中间表示，将其转换为可由商业求解器使用的格式。在本报告中，我们介绍了NeurIPS 2022竞赛的LP单词问题数据集和共享任务。此外，我们调查和比较ChatGPT大语言模型的性能对获胜的解决方案。通过这次比赛，我们希望能够为开发新的机器学习应用程序和优化建模数据集带来兴趣。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
