Summary of Not All Adapters Matter: Selective Adapter Freezing For Memory-efficient Fine-tuning Of Language Models, by Hyegang Son et al.
Not All Adapters Matter: Selective Adapter Freezing for Memory-Efficient Fine-Tuning of Language Models
by Hyegang Son, Yonglak Son, Changhoon Kim, Young Geun Kim
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed SAFE method, a variant of adapter-tuning for transformer-based pre-trained models, addresses the inefficiencies of traditional fine-tuning and adapter-tuning approaches. By gradually freezing less-important adapters during early training steps, SAFE reduces memory usage, computation amount, and training time by 42.85%, 34.59%, and 11.82%, respectively, while achieving comparable or better performance compared to the baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformer-based pre-trained models are very successful, but fine-tuning them takes a lot of resources. Researchers have found ways to use less resources, like adapter-tuning. But they haven’t all been created equal. Some adapters don’t really help much. The new SAFE method is designed to deal with this by freezing the unimportant adapters early on in the training process. This helps save memory, processing power, and time without sacrificing performance. |
Keywords
» Artificial intelligence » Fine tuning » Transformer
