Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Authors: 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
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments on three benchmark datasets. Our results demonstrate that AUGPE produces DP synthetic text that yields competitive utility with the SOTA DP finetuning baselines. |
| Researcher Affiliation | Collaboration | 1University of Illinois Urbana-Champaign 2Microsoft Research 3Sun Yat-sen University 4University of Chicago. |
| Pseudocode | Yes | Algorithm 1 Augmented Private Evolution (AUG-PE) |
| Open Source Code | Yes | Our code and data are available at https://github.com/AI-secure/aug-pe. |
| Open Datasets | Yes | Datasets. We evaluate AUG-PE on three datasets: Yelp Review (Inc, 2023), Open Review, and Pub Med abstracts. |
| Dataset Splits | Yes | The number of train/val/test samples and label information in Tb. 10. |
| Hardware Specification | Yes | it takes 1764 GPU hours on 32G NVIDIA V100 to finetune GPT-2-Large on Yelp |
| Software Dependencies | No | The paper mentions specific models (e.g., 'sentencetransformer', 'Ro BERTa-base', 'BERTMini and BERTSmall') and their developers/citations, but it does not provide specific version numbers for general software components or libraries (e.g., Python, PyTorch). |
| Experiment Setup | Yes | We set the max sequence length as 512, the batch size as 64, the learning rate as 3e-5, and the number of epochs as 5 for Yelp and 10 for Open Review. |