ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling - Huang, Chenyu; Tang, Zhengyang; Hu, Shixi; Jiang, Ruoqing; Zheng, Xin; Ge, Dongdong; Wang, Benyou; Wang, Zizhuo (Operations Research, 2025)
ORLM: Pioneering Open-Source Framework for Automated Optimization Modeling A study titled "ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling" has been published, introducing the first open-source framework designed to automate optimization modeling using large language models (LLMs). This innovative approach addresses critical challenges in the field of operations research (OR), particularly the overreliance on closed-source LLMs like GPT-4, which raises privacy concerns and limits customization in industrial applications. The research team proposed OR-Instruct, a semiautomated data synthesis framework that generates high-quality training data tailored to specific optimization modeling requirements. They also introduced IndustryOR, the first benchmark for evaluating LLMs’ performance on real-world OR problems. By training several 7B-scale open-source LLMs with the synthesized data, the team achieved state-of-the-art results across multiple benchmarks, including NL4Opt, MAMO, and IndustryOR. This advancement not only enhances the accessibility and applicability of optimization modeling, but also paves the way for more efficient and privacy-conscious solutions in various industrial sectors. The ORLM framework exemplifies the potential of open-source initiatives in driving innovation and democratizing advanced analytical tools for operations research.