Summary of Differentially Private Learning Needs Better Model Initialization and Self-distillation, by Ivoline C. Ngong et al.
Differentially Private Learning Needs Better Model Initialization and Self-Distillation
by Ivoline C. Ngong, Joseph P. Near, Niloofar Mireshghallah
First submitted to arxiv on: 23 Oct 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 This paper introduces DPRefine, a three-phase method for training language models while preserving differential privacy. The approach uses data synthesis from a small pre-trained model to initialize the process, followed by differential privacy finetuning on private data, and concludes with self-distillation to refine output quality. Compared to vanilla DPSGD, DPRefine significantly outperforms in terms of utility, diversity, and linguistic quality, as evaluated by AlpacaEval across various datasets. The method reduces linguistic errors by 84.0%, mitigating grammar and spelling mistakes commonly associated with DPSGD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DPRefine is a new way to train language models while keeping people’s data private. This method uses small models like GPT-2 as a starting point, then makes adjustments based on private data, and finally refines the results to make them more accurate and less likely to contain errors. By using this approach, DPRefine can generate text that is better than what other methods produce when it comes to grammar, spelling, and overall quality. |
Keywords
» Artificial intelligence » Distillation » Gpt
