Summary of Learning to Refuse: Towards Mitigating Privacy Risks in Llms, by Zhenhua Liu et al.
Learning to Refuse: Towards Mitigating Privacy Risks in LLMs
by Zhenhua Liu, Tong Zhu, Chuanyuan Tan, Wenliang Chen
First submitted to arxiv on: 14 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 This study addresses the challenge of enabling large language models to protect private information without requiring complete retraining. Researchers propose a Real-world pErsonal daTa UnleaRNing dataset (return) and introduce the Name-Aware Unlearning Framework (NAUF) for Privacy Protection. NAUF enables the model to learn which individuals’ information should be protected while maintaining its ability to answer questions related to other unrelated individuals. The study demonstrates that NAUF achieves state-of-the-art average unlearning scores, surpassing the best baseline method by 5.65 points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can understand and generate natural language, but they can also memorize private information, posing privacy risks. This study creates a dataset called return to test machine unlearning methods that protect personal data without retraining the model. The researchers also develop a framework called NAUF that helps the model learn which people’s information to keep private while still answering questions about other topics. |
