47 citations · 145 across the 24 of their papers we have counts for
6 papers · 1 filter
Planting and Mitigating Memorized Content in Predictive-Text Language Models
C. M. Downey, Wei Dai, Huseyin A. Inan +3
Language models are widely deployed to provide automatic text completion services in user products. However, recent research has revealed that language models (especially large one…
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Xiang Yue, Huseyin A. Inan, Xuechen Li +6
Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating syntheti…
Combing for Credentials: Active Pattern Extraction from Smart Reply
Bargav Jayaraman, Esha Ghosh, Melissa Chase +3
Pre-trained large language models, such as GPT\nobreakdash-2 and BERT, are often fine-tuned to achieve state-of-the-art performance on a downstream task. One natural example is the…
When Does Differentially Private Learning Not Suffer in High Dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto +4
Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models. A common theme in these results is the surprising observation tha…
Privacy Leakage in Text Classification: A Data Extraction Approach
Adel Elmahdy, Huseyin A. Inan, Robert Sim
Recent work has demonstrated the successful extraction of training data from generative language models. However, it is not evident whether such extraction is feasible in text clas…
Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin A Inan +2
Recent papers have shown that large pre-trained language models (LLMs) such as BERT, GPT-2 can be fine-tuned on private data to achieve performance comparable to non-private models…