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09/21/2023
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
We study the problem of in-context learning (ICL) with large language mo...
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12/16/2022
Planting and Mitigating Memorized Content in Predictive-Text Language Models
Language models are widely deployed to provide automatic text completion...
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10/25/2022
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Privacy concerns have attracted increasing attention in data-driven prod...
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07/01/2022
When Does Differentially Private Learning Not Suffer in High Dimensions?
Large pretrained models can be privately fine-tuned to achieve performan...
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06/09/2022
Privacy Leakage in Text Classification: A Data Extraction Approach
Recent work has demonstrated the successful extraction of training data ...
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06/03/2022
Differentially Private Model Compression
Recent papers have shown that large pre-trained language models (LLMs) s...
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10/13/2021
Differentially Private Fine-tuning of Language Models
We give simpler, sparser, and faster algorithms for differentially priva...
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06/21/2021
Membership Inference on Word Embedding and Beyond
In the text processing context, most ML models are built on word embeddi...
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03/12/2021
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Neural language models are known to have a high capacity for memorizatio...
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01/14/2021
Privacy Analysis in Language Models via Training Data Leakage Report
Recent advances in neural network based language models lead to successf...
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05/21/2020
rTop-k: A Statistical Estimation Approach to Distributed SGD
The large communication cost for exchanging gradients between different ...
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09/27/2019
Improving Semantic Parsing with Neural Generator-Reranker Architecture
Semantic parsing is the problem of deriving machine interpretable meanin...
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08/04/2018
