Summary of Learnable Privacy Neurons Localization in Language Models, by Ruizhe Chen et al.
Learnable Privacy Neurons Localization in Language Models
by Ruizhe Chen, Tianxiang Hu, Yang Feng, Zuozhu Liu
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 approach for identifying and mitigating privacy risks in Large Language Models (LLMs) is presented. The study focuses on pinpointing PII-sensitive neurons, also known as privacy neurons, within LLMs that memorize Personally Identifiable Information (PII). A learnable binary weight mask method is introduced to localize specific neurons responsible for PII memorization through adversarial training. The investigation reveals that a small subset of neurons across all layers are involved in PII specificity. Additionally, the potential effectiveness of deactivating localized privacy neurons for PII risk mitigation is explored. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have been found to remember private information, like Personally Identifiable Information (PII). This raises concerns about how this happens and what can be done to stop it. Researchers developed a new way to find the specific parts of LLMs that store PII. They used special masks to identify the neurons responsible for remembering PII. The study shows that only a small group of neurons is involved in storing PII, and that by turning these neurons off, PII risk can be reduced. |
Keywords
» Artificial intelligence » Mask
