Summary of Adarank: Disagreement Based Module Rank Prediction For Low-rank Adaptation, by Yihe Dong
AdaRank: Disagreement Based Module Rank Prediction for Low-rank Adaptation
by Yihe Dong
First submitted to arxiv on: 16 Aug 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 This paper introduces AdaRank, a novel approach to finetuning large language models for specific tasks. By predicting the optimal rank of model modules relative to each other, AdaRank improves generalization on unseen data compared to traditional uniform rank methods. The technique is simple and doesn’t require any additional objectives or regularizers, making it compatible with existing pretraining and adaptation stages. Experimental results demonstrate that AdaRank outperforms prior work while maintaining the same number of parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have developed a new way to fine-tune large language models for specific tasks. They found that some parts of these models are more important than others, so they created a method called AdaRank to figure out which parts need to change and how much. This helps the model learn better and make fewer mistakes on new data. The best part is that this approach doesn’t require any special additional training or rules, making it easy to use with existing models. |
Keywords
» Artificial intelligence » Generalization » Pretraining
