Summary of Parameter-efficient Fine-tuning with Adapters, by Keyu Chen et al.
Parameter-Efficient Fine-Tuning With Adapters
by Keyu Chen, Yuan Pang, Zi Yang
First submitted to arxiv on: 9 May 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 This paper introduces a novel adaptation method for language models, which significantly reduces computational intensity while maintaining competitive performance across various benchmarks. The approach, based on the UniPELT framework and a PromptTuning Layer, employs adapters to enable efficient transfer of pretrained models to new tasks with minimal retraining of base model parameters. Evaluations using three diverse datasets demonstrate that the customized adapter-based method achieves performance comparable to full model fine-tuning, DAPT+TAPT, and UniPELT strategies while requiring fewer or equivalent parameters. The study highlights the potential of adapters in achieving high performance with significantly reduced resource consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for language models to learn new tasks without using up too much computer power. It does this by building on an existing method called UniPELT and adding a special layer that helps fine-tune the model quickly and efficiently. The researchers tested their approach on three different sets of data and found that it worked just as well as more complex methods, but required fewer calculations. This could be important for training language models in the future. |
Keywords
* Artificial intelligence * Fine tuning
