Summary of Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?, by Nader Asadi et al.
Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?
by Nader Asadi, Mahdi Beitollahi, Yasser Khalil, Yinchuan Li, Guojun Zhang, Xi Chen
First submitted to arxiv on: 23 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 The paper explores the composability of pre-trained LoRA modules, which have become a standard for efficient fine-tuning of large language and vision models on downstream tasks. It investigates two approaches: uniform composition, where upstream modules are averaged with equal weights, and learned composition, where the weights are learned to perform weighted averaging. The study reveals that both methods outperform full fine-tuning and training a LoRA from scratch in few-shot settings, while learned composition performs comparably to regular LoRA training with fewer trainable parameters in full-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoRA modules have become super-efficient for fine-tuning large models on specific tasks. This paper looks at how well we can combine these pre-trained modules to get even better results on new tasks. They tried two ways: one where all the modules are mixed equally, and another where they learn which module to use most. The results show that both methods work better than starting from scratch or fine-tuning everything again. It’s like having a superpowerful toolbox! |
Keywords
* Artificial intelligence * Few shot * Fine tuning * Lora
