Summary of Fine-tuning Language Models with Differential Privacy Through Adaptive Noise Allocation, by Xianzhi Li et al.
Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation
by Xianzhi Li, Ran Zmigrod, Zhiqiang Ma, Xiaomo Liu, Xiaodan Zhu
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 Medium Difficulty summary: This paper introduces ANADP, an algorithm that adaptively allocates additive noise to model parameters based on their importance in preserving privacy. Traditional differential privacy approaches often overlook individual parameter sensitivities and contributions, leading to suboptimal models. ANADP narrows the performance gap between regular fine-tuning and traditional DP fine-tuning on various datasets while maintaining required privacy constraints. The proposed method addresses limitations of existing approaches by considering distinct parameter importances in privacy protection. The paper contributes to the development of more effective language model training methods that balance performance and privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research proposes a new way to train language models while keeping people’s private information safe. Right now, some language models can store lots of detailed information, which is both useful and concerning. The current methods for protecting privacy work, but they’re not very efficient or effective. The new algorithm, called ANADP, adjusts the amount of noise added to the model based on how important each piece of information is. This helps to find a good balance between keeping private data safe and allowing the language model to be useful. |
Keywords
» Artificial intelligence » Fine tuning » Language model
