Summary of Parameter Efficient Fine Tuning: a Comprehensive Analysis Across Applications, by Charith Chandra Sai Balne et al.
Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications
by Charith Chandra Sai Balne, Sreyoshi Bhaduri, Tamoghna Roy, Vinija Jain, Aman Chadha
First submitted to arxiv on: 21 Apr 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 The paper explores Parameter Efficient Fine-Tuning (PEFT) techniques, which selectively update parameters to balance computational efficiency with performance. It reviews various PEFT strategies, highlighting applications across domains such as text generation, medical imaging, protein modeling, and speech synthesis. The study assesses the effectiveness of PEFT methods in reducing computational load, speeding up training, and lowering memory usage, aiming to contribute to making deep learning more accessible and adaptable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make AI models learn faster and use less computer power. It compares different ways to do this called Parameter Efficient Fine-Tuning (PEFT). PEFT helps by only changing certain parts of the model instead of all of it. The paper shows how PEFT can be used in things like text generation, medical imaging, and speech synthesis. It wants to help make AI models easier to use and faster to train, so they can be used more often. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Parameter efficient » Text generation
