Summary of Lora-drop: Efficient Lora Parameter Pruning Based on Output Evaluation, by Hongyun Zhou et al.
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation
by Hongyun Zhou, Xiangyu Lu, Wang Xu, Conghui Zhu, Tiejun Zhao, Muyun Yang
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 parameter-efficient fine-tuning called LoRA-drop, which addresses the challenge of scaling up pre-trained models under limited computing resources. The method introduces an adaptive mechanism that selectively retains and shares LoRA (Low-Rank Adaptation) parameters based on their importance, as measured by the output of LoRA elements. By retaining only crucial LoRA parameters, LoRA-drop achieves performance comparable to full fine-tuning and LoRA while reducing the computational cost by up to 50%. The proposed method is evaluated on various natural language understanding (NLU) and generation (NLG) tasks, demonstrating its effectiveness in improving model efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make pre-trained models work better with less computing power. They found that some parts of the model are more important than others and can be shared or removed without hurting performance. This new method, called LoRA-drop, helps the model learn better by keeping only the most useful parts and sharing the rest. It works well on lots of language tasks and makes the model use fewer resources. |
Keywords
* Artificial intelligence * Fine tuning * Language understanding * Lora * Low rank adaptation * Parameter efficient
