Summary of Towards Robust Knowledge Unlearning: An Adversarial Framework For Assessing and Improving Unlearning Robustness in Large Language Models, by Hongbang Yuan et al.
Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models
by Hongbang Yuan, Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 presents a dynamic and automated framework for attacking language learning models (LLMs) to evaluate their robustness against unwanted knowledge. The Dynamic Unlearning Attack (DUA) optimizes adversarial suffixes to reintroduce unlearned knowledge in various scenarios, revealing vulnerabilities in existing unlearning methods. Despite manual design of attack queries, the unlearned knowledge can still be recovered in 55.2% of questions without revealing model parameters. To address this vulnerability, the paper proposes Latent Adversarial Unlearning (LAU), a universal framework that enhances robustness through min-max optimization. LAU consists of an attack stage and defense stage, training perturbation vectors to recover unlearned knowledge and enhance model robustness. The proposed methods, AdvGA and AdvNPO, demonstrate improved unlearning effectiveness by 53.5%, minimal impact on neighboring knowledge (11.6%), and no significant effect on models’ general capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language learning machines better at forgetting bad things they learned. Right now, these machines can get stuck with unwanted knowledge from their training data. The authors developed a new way to test how well these machines forget by creating fake questions that trick them into remembering the bad stuff. They found that this method works in most cases! To make it even better, they created another system that helps the machine forget faster and more effectively without losing its ability to learn. |
Keywords
» Artificial intelligence » Optimization
