A Systematic Survey on Large Language Models for Algorithm Design
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909980105375744 |
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| author | Liu, Fei Yao, Yiming Guo, Ping Yang, Zhiyuan Zhao, Zhe Lin, Xi Tong, Xialiang Mao, Kun Lu, Zhichao Wang, Zhenkun Yuan, Mingxuan Zhang, Qingfu |
| author_facet | Liu, Fei Yao, Yiming Guo, Ping Yang, Zhiyuan Zhao, Zhe Lin, Xi Tong, Xialiang Mao, Kun Lu, Zhichao Wang, Zhenkun Yuan, Mingxuan Zhang, Qingfu |
| contents | Algorithm design is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising solutions. In just a few years, this integration has yielded remarkable progress in areas ranging from combinatorial optimization to scientific discovery. Despite this rapid expansion, a holistic understanding of the field is hindered by the lack of a systematic review, as existing surveys either remain limited to narrow sub-fields or with different objectives. This paper seeks to provide a systematic review of algorithm design with LLMs. We introduce a taxonomy that categorises the roles of LLMs as optimizers, predictors, extractors and designers, analyzing the progress, advantages, and limitations within each category. We further synthesize literature across the three phases of the algorithm design pipeline and across diverse algorithmic applications that define the current landscape. Finally, we outline key open challenges and opportunities to guide future research. To support future research and collaboration, we provide an accompanying repository at: https://github.com/FeiLiu36/LLM4AlgorithmDesign. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14716 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Systematic Survey on Large Language Models for Algorithm Design Liu, Fei Yao, Yiming Guo, Ping Yang, Zhiyuan Zhao, Zhe Lin, Xi Tong, Xialiang Mao, Kun Lu, Zhichao Wang, Zhenkun Yuan, Mingxuan Zhang, Qingfu Machine Learning Artificial Intelligence Computation and Language Algorithm design is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising solutions. In just a few years, this integration has yielded remarkable progress in areas ranging from combinatorial optimization to scientific discovery. Despite this rapid expansion, a holistic understanding of the field is hindered by the lack of a systematic review, as existing surveys either remain limited to narrow sub-fields or with different objectives. This paper seeks to provide a systematic review of algorithm design with LLMs. We introduce a taxonomy that categorises the roles of LLMs as optimizers, predictors, extractors and designers, analyzing the progress, advantages, and limitations within each category. We further synthesize literature across the three phases of the algorithm design pipeline and across diverse algorithmic applications that define the current landscape. Finally, we outline key open challenges and opportunities to guide future research. To support future research and collaboration, we provide an accompanying repository at: https://github.com/FeiLiu36/LLM4AlgorithmDesign. |
| title | A Systematic Survey on Large Language Models for Algorithm Design |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2410.14716 |