Summary of Enhancing Parameter Efficiency and Generalization in Large-scale Models: a Regularized and Masked Low-rank Adaptation Approach, by Yuzhu Mao et al.
Enhancing Parameter Efficiency and Generalization in Large-Scale Models: A Regularized and Masked Low-Rank Adaptation Approach
by Yuzhu Mao, Siqi Ping, Zihao Zhao, Yang Liu, Wenbo Ding
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 method, Regularized and Masked Low-Rank Adaptation (RM-LoRA), improves upon the existing Low-Rank Adaptation (LoRA) technique by increasing the intrinsic dimension of matrix updates. This modification enables RM-LoRA to achieve superior generalization performance with a reduced trainable parameter budget compared to LoRA and its variants, as demonstrated across various open-source vision and language datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large pre-trained models like large language models (LLMs) are challenging to fine-tune due to their extensive size. To reduce resource consumption while maintaining satisfactory results, the Low-Rank Adaptation (LoRA) method was developed. However, it has limitations, including suboptimal performance and overfitting. This paper investigates the benefits of increasing the intrinsic dimension in LoRA and proposes a new method called Regularized and Masked LoRA (RM-LoRA). RM-LoRA achieves better results with fewer trainable parameters than previous methods across different datasets. |
Keywords
» Artificial intelligence » Generalization » Lora » Low rank adaptation » Overfitting
