Summary of Finegates: Llms Finetuning with Compression Using Stochastic Gates, by Jonathan Svirsky et al.
FineGates: LLMs Finetuning with Compression using Stochastic Gates
by Jonathan Svirsky, Yehonathan Refael, Ofir Lindenbaum
First submitted to arxiv on: 17 Dec 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 This paper tackles the challenge of fine-tuning Large Language Models (LLMs) with billions of parameters, which require significant computational resources and memory. The authors propose a novel approach that utilizes stochastic gates to adaptively sparsify the frozen base model while training only a few additional parameters. This method reduces resource usage and mitigates overfitting risks. The authors evaluate their approach using several recent baselines and show improved accuracy compared to these baselines, allowing for up to 20-40% reduction in trainable parameters without significant accuracy loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make Large Language Models work better when we don’t have enough computer power or data. The problem is that these models are really big and require lots of resources to train. To solve this, the authors came up with an idea called “stochastic gates” that helps the model learn new things while still being efficient. They tested their idea and found it worked well, even when they reduced the number of parameters the model needed to learn. |
Keywords
» Artificial intelligence » Fine tuning » Overfitting
