Summary of Pemt: Multi-task Correlation Guided Mixture-of-experts Enables Parameter-efficient Transfer Learning, by Zhisheng Lin et al.
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning
by Zhisheng Lin, Han Fu, Chenghao Liu, Zhuo Li, Jianling Sun
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 In this paper, researchers introduce a novel approach to adapting pre-trained language models called PEMT (Parameter-Efficient Multi-Task Transfer Learning). PEMT combines the strengths of parameter-efficient fine-tuning with multi-task transfer learning to improve performance on various tasks. The framework uses a gated unit to determine the weights for transferring knowledge from source tasks, and a Task Sparsity Loss to encourage task-specific learning. The authors demonstrate the effectiveness of PEMT through experiments on 17 datasets across various tasks, achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PEMT is a new way to help computers learn from one task to do better on another task. It’s like taking what you learned in school and applying it to other subjects. The researchers made a special tool called PEMT that helps computers learn this way. They tested it on many different tasks and showed that it works really well. |
Keywords
* Artificial intelligence * Fine tuning * Multi task * Parameter efficient * Transfer learning
