Summary of Merging Multi-task Models Via Weight-ensembling Mixture Of Experts, by Anke Tang et al.
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
by Anke Tang, Li Shen, Yong Luo, Nan Yin, Lefei Zhang, Dacheng Tao
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposes a novel approach to merge various task-specific Transformer-based models trained on different tasks into a single unified model that can execute all the tasks concurrently. Building upon previous methods, such as task arithmetic, which have been shown to be effective and scalable, this work focuses on mitigating the interference between parameters of different models, which can significantly degrade performance. The authors introduce a weight-ensembling mixture of experts (MoE) module that dynamically integrates shared and task-specific knowledge based on input. This approach allows for a more flexible solution that adapts to the specific needs of each instance. Experimental results demonstrate the effectiveness of this method in terms of generalization and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for machines to do many tasks at the same time. Usually, we combine different models together to make one supermodel that can do all these tasks. However, this can cause problems because some parts of the model might not work well with others. The authors found a way to solve this problem by creating a new module that combines the good things from each model and uses them in a smart way. This makes the supermodel more flexible and better at doing different tasks. |
Keywords
* Artificial intelligence * Generalization * Mixture of experts * Transformer
