Summary of Efficient and Effective Weight-ensembling Mixture Of Experts For Multi-task Model Merging, by Li Shen et al.
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging
by Li Shen, Anke Tang, Enneng Yang, Guibing Guo, Yong Luo, Lefei Zhang, Xiaochun Cao, Bo Du, Dacheng Tao
First submitted to arxiv on: 29 Oct 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 multi-task learning called Weight-Ensembling Mixture of Experts (WEMoE). The authors leverage recent advances in task arithmetic-based MTL to develop a method that merges the parameters of independently fine-tuned models. WEMoE identifies critical modules by analyzing parameter variations and then statically merges non-critical modules while transforming critical modules into a mixture-of-experts (MoE) structure. During inference, expert modules are dynamically merged based on input samples. The authors also introduce an efficient-and-effective variant called E-WEMoE, which reduces the trainable parameters, overall parameter count, and computational overhead of the merged model. Experimental results demonstrate that both WEMoE and E-WEMoE outperform state-of-the-art (SOTA) model merging methods in terms of MTL performance, generalization, and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers learn many things at once. They use a technique called multi-task learning, which helps different tasks share knowledge with each other. The authors came up with a method that takes the good parts from different models and combines them into one better model. This new model can adapt to different situations by changing how it weighs different pieces of information. The authors tested their method on many different types of models and tasks, and it worked better than what was already out there. |
Keywords
» Artificial intelligence » Generalization » Inference » Mixture of experts » Multi task
