Summary of Fine-tuning Aligned Classifiers For Merging Outputs: Towards a Superior Evaluation Protocol in Model Merging, by Fanshuang Kong et al.
Fine-tuning Aligned Classifiers for Merging Outputs: Towards a Superior Evaluation Protocol in Model Merging
by Fanshuang Kong, Richong Zhang, Zhijie Nie, Ziqiao Wang, Qiang Sun
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Model merging combines multiple fine-tuned models into a single one via parameter fusion, achieving improvements across many tasks. However, in the classification task, we find a misalignment issue between merging outputs and the fine-tuned classifier, which limits its effectiveness. The study observes that merging outputs exhibit comparable cluster effects with fine-tuned outputs and already contain necessary classification information, while the misalignment can converge to an orthogonal transformation. Alleviating this misalignment can significantly enhance the performance of merging models. To address this issue, the paper proposes a new protocol FT-Classifier, which fine-tunes an aligned classifier with few-shot unlabeled samples, enabling better evaluation of merging methods and improved classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, scientists have found that combining multiple models into one can be helpful for many tasks. However, there’s a problem when using these combined models for classification (identifying categories). This issue makes it harder to get good results. The researchers looked at the data and saw that the combined models already contain the information needed for classification, but the misalignment is causing problems. To solve this, they proposed a new way to fine-tune the classifier, which can improve performance. |
Keywords
» Artificial intelligence » Classification » Few shot
