用大模型自动建模优化问题，提升决策效率。
A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions
- 利用大模型自动生成数学优化模型，降低专业门槛。
- 发现基准数据集错误率高达30%，清洗后构建新评测榜单。
- 开源工具平台整合代码、论文与清洗数据，促进行业共享。
优化建模在诸多领域用于最优决策，但依赖运筹学专业人才。随着大语言模型（LLMs）的兴起，自动化数学建模成为可能。本综述系统梳理了从基础模型微调、推理框架、基准数据集到性能评估的全技术链进展。我们深入分析了基准数据集质量，发现其错误率高达30%。通过清洗数据并构建公平评测体系，更新了基于原始模型和数据集的性能排行榜。同时搭建了在线资源门户，集成清洗后的数据集、代码库与论文链接，推动社区协作。最后，指出现有方法局限，并提出未来研究方向。
原文摘要 · Abstract (English)
By virtue of its great utility in solving real-world problems, optimization modeling has been widely employed for optimal decision-making across various sectors, but it requires substantial expertise from operations research professionals. With the advent of large language models (LLMs), new opportunities have emerged to automate the procedure of mathematical modeling. This survey presents a comprehensive and timely review of recent advancements that cover the entire technical stack, including data synthesis and fine-tuning for the base model, inference frameworks, benchmark datasets, and performance evaluation. In addition, we conducted an in-depth analysis on the quality of benchmark datasets, which was found to have a surprisingly high error rate. We cleaned the datasets and constructed a new leaderboard with fair performance evaluation in terms of base LLM model and datasets. We also build an online portal that integrates resources of cleaned datasets, code and paper repository to benefit the community. Finally, we identify limitations in current methodologies and outline future research opportunities.
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