Summary of Parameter-efficient Fine-tuning For Large Models: a Comprehensive Survey, by Zeyu Han et al.
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
by Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, Sai Qian Zhang
First submitted to arxiv on: 21 Mar 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 A Large Language Model Fine-Tuning Framework: Parameter Efficient Fine-Tuning (PEFT) is a novel approach that efficiently adjusts large language models for specific tasks while minimizing additional parameters and computational resources. This framework addresses the challenges of fine-tuning large-scale language models, which are computationally expensive and resource-intensive. PEFT algorithms offer improved performance and reduced computation costs, making it an essential tool for researchers and developers. Applications of PEFT include natural language processing, question answering, text classification, and sentiment analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models Need Help: These big models do amazing things, but they are really hard to use because they need so much computer power. That’s a problem when you want to make them work for specific tasks. A new way of making these models work better is called Parameter Efficient Fine-Tuning (PEFT). It helps make the models work faster and use less computer power. PEFT is important for people who study and develop language models. |
Keywords
* Artificial intelligence * Fine tuning * Large language model * Natural language processing * Parameter efficient * Question answering * Text classification
