NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
Abstract
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.
Cite
@article{arxiv.2303.08233,
title = {NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions},
author = {Rindranirina Ramamonjison and Timothy T. Yu and Raymond Li and Haley Li and Giuseppe Carenini and Bissan Ghaddar and Shiqi He and Mahdi Mostajabdaveh and Amin Banitalebi-Dehkordi and Zirui Zhou and Yong Zhang},
journal= {arXiv preprint arXiv:2303.08233},
year = {2023}
}