Summary of Beyond Efficiency: a Systematic Survey Of Resource-efficient Large Language Models, by Guangji Bai et al.
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
by Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, Xinyuan Song, Carl Yang, Yue Cheng, Liang Zhao
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The survey aims to address challenges faced by Large Language Models (LLMs) like OpenAI’s ChatGPT by reviewing techniques that enhance their resource efficiency. The paper categorizes methods based on their optimization focus: computational, memory, energy, financial, and network resources across various stages of an LLM’s lifecycle. It also introduces a nuanced categorization of resource efficiency techniques, uncovering relationships between different resources and corresponding optimizations. A standardized set of evaluation metrics and datasets is presented for consistent comparisons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human-like text. They use lots of computing power, memory, energy, and money. This paper looks at ways to make these models more efficient so they can work better in environments with limited resources. It groups different techniques into categories based on what they optimize: computer speed, memory use, energy consumption, financial cost, and network connections. The paper also introduces a new way of thinking about resource efficiency and provides tools for comparing different models and techniques. |
Keywords
* Artificial intelligence * Optimization
