Summary of A Systematic Survey on Large Language Models For Algorithm Design, by Fei Liu et al.
A Systematic Survey on Large Language Models for Algorithm Design
by Fei Liu, Yiming Yao, Ping Guo, Zhiyuan Yang, Zhe Zhao, Xi Lin, Xialiang Tong, Mingxuan Yuan, Zhichao Lu, Zhenkun Wang, Qingfu Zhang
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides a systematic review of the integration of Large Language Models (LLMs) into Algorithm Design (AD), also known as LLM4AD. The authors summarize existing studies and introduce a taxonomy to categorize the literature across four dimensions: roles of LLMs, search methods, prompt methods, and application domains. They discuss the potential and achievements of LLMs in AD, highlighting promising directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews how big language models can help with designing algorithms. It looks at what’s been done so far, what works well, and where there are still challenges. The researchers found that these language models have helped with things like optimization, machine learning, and scientific discovery. They also identify areas where more work is needed. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Prompt
