Summary of Parameter-efficient Fine-tuning in Large Models: a Survey Of Methodologies, by Luping Wang et al.
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies
by Luping Wang, Sheng Chen, Linnan Jiang, Shu Pan, Runze Cai, Sen Yang, Fei Yang
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper explores Parameter-Efficient Fine-Tuning (PEFT), a solution to adapt large pre-trained models for specific downstream tasks while minimizing additional parameters and computational resources. The authors discuss how large models have made significant progress in natural language generation tasks, but their massive scale poses challenges for fine-tuning on hardware platforms with limited power. PEFT adjusts the parameters of pre-trained models to suit various tasks or domains, achieving efficient adaptation without introducing excessive parameters. This review provides an overview of PEFT’s core ideas, principles, applications, and potential future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large models have made huge progress in many areas, especially natural language generation tasks. They’re really good at understanding human language! However, these big models need a lot of computer power and memory to work. When we want to use them for specific tasks, it’s hard because they require so much computing power and memory. To solve this problem, the paper introduces Parameter-Efficient Fine-Tuning (PEFT). PEFT helps make these big models smaller and more efficient, so they can be used on regular computers instead of super-powerful machines. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
