系统梳理大模型在组合优化中的应用现状与未来方向
Large Language Models for Combinatorial Optimization: A Systematic Review
- 按PRISMA指南筛选2000+文献，最终纳入103篇研究
- 归纳出大模型在组合优化中的任务类型、架构与专用数据集
- 适合关注AI与运筹学交叉的科研人员参考
本系统综述研究了大语言模型（LLMs）在组合优化（CO）中的应用。依据系统综述与元分析首选报告项目（PRISMA）指南，通过Scopus和Google Scholar开展文献检索，共考察2000余篇论文。根据语言、研究焦点、发表年份及文献类型等四类纳入标准与四类排除标准进行评估，最终选定103项研究。将这些研究按语义类别与主题分类，全面呈现该领域的研究进展，包括大模型执行的任务、采用的架构、专用于评估大模型在组合优化中表现的数据集及其应用场景。最后，提出该领域未来的研究方向。
原文摘要 · Abstract (English)
This systematic review explores the application of Large Language Models (LLMs) in Combinatorial Optimization (CO). We report our findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conduct a literature search via Scopus and Google Scholar, examining over 2,000 publications. We assess publications against four inclusion and four exclusion criteria related to their language, research focus, publication year, and type. Eventually, we select 103 studies. We classify these studies into semantic categories and topics to provide a comprehensive overview of the field, including the tasks performed by LLMs, the architectures of LLMs, the existing datasets specifically designed for evaluating LLMs in CO, and the field of application. Finally, we identify future directions for leveraging LLMs in this field.
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