Summary of Hadamard Adapter: An Extreme Parameter-efficient Adapter Tuning Method For Pre-trained Language Models, by Yuyan Chen et al.
Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models
by Yuyan Chen, Qiang Fu, Ge Fan, Lun Du, Jian-Guang Lou, Shi Han, Dongmei Zhang, Zhixu Li, Yanghua Xiao
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper introduces a novel adapter for pre-trained language models (PLMs) to reduce their parameters without compromising performance on downstream tasks. The adapter, called the Hadamard adapter, uses element-wise linear transformation and requires fewer parameters than previous adapters. The authors also provide tuning patterns for the adapter that can be shared across various tasks, offering guidance for future studies. Experimental results on the GLUE benchmark with several state-of-the-art PLMs demonstrate competitive performance with only 0.033% of the original model’s parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make big language models smaller without making them worse. These models have many parameters, which makes it hard and expensive to use them in different tasks. The authors created an adapter that helps make these models more efficient by changing how they pay attention to information. They also shared some tips for using this adapter that can be useful for other researchers. The results show that their approach works well on a popular benchmark with many state-of-the-art language models, and it uses much fewer parameters than the original model. |
Keywords
* Artificial intelligence * Attention
