Summary of See Further For Parameter Efficient Fine-tuning by Standing on the Shoulders Of Decomposition, By Chongjie Si et al.
See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition
by Chongjie Si, Xiaokang Yang, Wei Shen
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 abstract describes the development of parameter-efficient fine-tuning (PEFT) methods to overcome the limitations of large foundation models in terms of computational and storage costs. Recent success in PEFT has led to a need for deeper understanding of its fundamental principles, which is addressed by this paper’s comprehensive mathematical analysis. The authors introduce novel PEFT methods and a simple framework for enhancing performance across various applications. Empirical validations demonstrate the efficacy of these methods, showcasing theoretical validity and practical performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to make large foundation models more efficient to use. It looks at a technique called parameter-efficient fine-tuning (PEFT) that tries to find a balance between using many parameters and being able to adapt to different tasks. Researchers have been successful in developing PEFT methods, but the underlying principles of how they work are not well understood. This paper takes a step back and tries to understand these principles by breaking them down mathematically. The authors also introduce new ways to do PEFT that can be used across different applications. |
Keywords
* Artificial intelligence * Fine tuning * Parameter efficient
