Summary of Spafit: Stratified Progressive Adaptation Fine-tuning For Pre-trained Large Language Models, by Samir Arora et al.
SPAFIT: Stratified Progressive Adaptation Fine-tuning for Pre-trained Large Language Models
by Samir Arora, Liangliang Wang
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 research paper, the authors explore efficient fine-tuning methods for Transformer-based language models to adapt them to specific tasks. The current approach, full fine-tuning, requires significant computational power and storage, limiting its adoption. To address this issue, they propose Stratified Progressive Adaptation Fine-tuning (SPAFIT), a parameter-efficient fine-tuning method that localizes different linguistic knowledge types to specific model layers. Experiments on the GLUE benchmark demonstrate that SPAFIT outperforms other methods while adjusting only a fraction of parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making it easier and faster to train language models for specific tasks, like understanding natural language. The current way of doing this takes too much computer power and storage, so researchers are looking for more efficient ways. They came up with a new method called SPAFIT that helps the model learn from data better by assigning different parts of the knowledge to different layers of the model. Tests showed that this approach worked well on nine tasks. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient » Transformer
