Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.03637 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915869051846656 |
|---|---|
| author | Da Ros, Francesca Soprano, Michael Di Gaspero, Luca Roitero, Kevin |
| author_facet | Da Ros, Francesca Soprano, Michael Di Gaspero, Luca Roitero, Kevin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Combinatorial Optimization: A Systematic Review Da Ros, Francesca Soprano, Michael Di Gaspero, Luca Roitero, Kevin Artificial Intelligence 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. |
| title | Large Language Models for Combinatorial Optimization: A Systematic Review |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.03637 |