Large Language Models (LLMs) have emerged as powerful tools for complex
Operations Research (OR) in automating optimization modeling. However, current
methodologies heavily rely on prompt engineering (e.g., multi-agent
cooperation) with proprietary LLMs, raising data privacy concerns that could be
prohibitive in industry applications. To tackle this issue, we propose training
open-source LLMs for optimization modeling. We identify four critical
requirements for the training dataset of OR LLMs, design and implement
OR-Instruct, a semi-automated process for creating synthetic data tailored to
specific requirements. We also introduce the IndustryOR benchmark, the first
industrial benchmark for testing LLMs on solving real-world OR problems. We
apply the data from OR-Instruct to various open-source LLMs of 7b size (termed
as ORLMs), resulting in a significantly improved capability for optimization
modeling. Our best-performing ORLM achieves state-of-the-art performance on the
NL4OPT, MAMO, and IndustryOR benchmarks. Our code and data will be available at
https://github.com/Cardinal-Operations/ORLM.

通过训练开源的大型语言模型（LLMs）来处理自动化优化建模中的数据隐私问题，提出了定制化合成数据的半自动化过程（OR-Instruct），并在实际应用中实现了显著改进的优化建模能力。