Summary of Gift-sw: Gaussian Noise Injected Fine-tuning Of Salient Weights For Llms, by Maxim Zhelnin et al.
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
by Maxim Zhelnin, Viktor Moskvoretskii, Egor Shvetsov, Egor Venediktov, Mariya Krylova, Aleksandr Zuev, Evgeny Burnaev
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers introduce a novel parameter-efficient fine-tuning method called Gaussian noise Injected Fine Tuning of Salient Weights (GIFT-SW), which improves upon existing methods by selectively updating only the most important weights while introducing noise into less critical ones. This approach is designed to leverage recent findings that a small subset of model weights has a significant impact on performance. The authors also propose a generalized sensitivity metric to identify these key columns, which enables more efficient fine-tuning of large language models (LLMs). Experimental results demonstrate the superiority of GIFT-SW over full fine-tuning and other parameter-efficient methods under equivalent computational budgets, as well as its ability to recover performance after mixed-precision quantization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to use big language models. They found that some parts of the model are more important than others, so they created a new way to update just those parts. This new method, called GIFT-SW, is better than other ways and can even fix models that have been made smaller but still work well. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient » Precision » Quantization
