Summary of Lmeraser: Large Model Unlearning Through Adaptive Prompt Tuning, by Jie Xu et al.
LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
by Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 A novel machine unlearning approach called LMERaser is proposed to efficiently protect privacy in large-scale machine learning models. The approach tackles the challenges of entangled training data and complex model architectures, which lead to high computational costs for large models. LMERaser employs a divide-and-conquer strategy with adaptive prompt tuning to isolate data influence, partitioning the training dataset into public and private parts. Public data are used to train the model’s backbone, while private data are clustered based on diversity and optimized separately. This mechanism reduces unlearning costs without compromising accuracy compared to prior work. The approach achieves a 100-fold reduction in unlearning costs, making it suitable for large-scale applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LMERaser is an efficient way to protect privacy in big machine learning models. It helps solve problems with the data and model that make it hard to remove private information without losing accuracy. LMERaser does this by dividing the training data into public and private parts, then using different techniques to optimize each part separately. This makes it much faster and more accurate than other methods. |
Keywords
» Artificial intelligence » Machine learning » Prompt
