Summary of Differentially Private Synthetic Data Via Foundation Model Apis 2: Text, by Chulin Xie et al.
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
by Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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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 proposes an augmented algorithm, Aug-PE, to generate differentially private (DP) synthetic text without requiring model training. The approach utilizes API access to a large language model (LLM), such as GPT-3.5, and leverages the Private Evolution (PE) algorithm introduced by Lin et al. (2024) for generating DP synthetic images. By applying PE to the complex setting of text, Aug-PE achieves competitive utility with state-of-the-art (SOTA) DP finetuning baselines on three benchmark datasets. This work facilitates more accessible routes to privacy-preserving LLM applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep private text data private by creating fake versions that are safe to share. It’s like making a copy of a secret recipe book so you can give it out without giving away the real one. The researchers use a special algorithm called Aug-PE that works with big language models, but doesn’t need to be trained on the private data. They test it on three different datasets and show that it produces good results. |
Keywords
* Artificial intelligence * Gpt * Large language model
