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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 24·5 min read
Personalized Privacy Control in LLMs via Attention Head Intervention
TL;DR
Personalized privacy control in large language models (LLMs) is enhanced through user-specific disclosure preferences. The proposed method, Repair, significantly improves adherence to these preferences, reducing policy ignorance in models like Qwen2.5-7B and Gemma3-4B.
✦ Why It Matters
Implement personalized privacy controls in your LLM applications to enhance user trust and data protection.
Key Takeaways
- Incorporate user-specific privacy preferences in LLMs.
- Utilize P3Bench to evaluate privacy policy effectiveness.
- Apply Repair to improve model adherence to privacy policies.
- Monitor policy ignorance ratios to assess model performance.
How It Works
Repair modifies the attention heads of LLMs during inference, allowing the model to adjust its responses based on user-specific privacy policies. This intervention targets the model's decision-making process, ensuring that the output aligns with the user's disclosure preferences.