Summary of Unlearning As Multi-task Optimization: a Normalized Gradient Difference Approach with An Adaptive Learning Rate, by Zhiqi Bu et al.
Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate
by Zhiqi Bu, Xiaomeng Jin, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Mingyi Hong
First submitted to arxiv on: 29 Oct 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 Machine unlearning, which removes unwanted knowledge acquired by large language models (LLMs), is framed as a regularized multi-task optimization problem. The goal is to optimize both forgetting and model performance objectives. A normalized gradient difference (NGDiff) algorithm is introduced, allowing for better control over the trade-off between these objectives. An automatic learning rate scheduler is integrated into the approach. Theoretical analysis and empirical evaluation demonstrate the superior performance of NGDiff on the TOFU and MUSE datasets, with stable training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine unlearning helps remove unwanted knowledge learned by large language models. Imagine your brain having to “forget” things you don’t need anymore. This paper looks at machine unlearning from a new angle, using optimization techniques to balance what’s important to remember and what’s not. A special algorithm called NGDiff is introduced, which lets us control how much of each thing we focus on. The results show that this approach works better than others on specific datasets, with stable training. |
Keywords
* Artificial intelligence * Multi task * Optimization
