Summary of Twin-merging: Dynamic Integration Of Modular Expertise in Model Merging, by Zhenyi Lu et al.
Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging
by Zhenyi Lu, Chenghao Fan, Wei Wei, Xiaoye Qu, Dangyang Chen, Yu Cheng
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed Twin-Merging method effectively combines multiple task-specific models into a single multitask model without extra training, addressing the challenges of interference and heterogeneous data during testing. By modularizing knowledge into shared and exclusive components, compressing redundant information, and dynamically merging shared and task-specific knowledge based on input, Twin-Merging narrows the performance gap between merged and fine-tuned models, achieving an average improvement of 28.34% in absolute normalized score for discriminative tasks. The approach also surpasses the fine-tuned upper bound on generative tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to combine multiple language and vision task-specific models into one model without extra training. This helps with combining different knowledge from different areas. The method does this by breaking down the knowledge into shared parts that can be used in many areas, and exclusive parts that are specific to each area. It then combines these parts based on what kind of input it gets. This approach works well for both language and vision tasks, and can even do better than fine-tuned models on some tasks. |
