12 citations · 29 across the 5 of their papers we have counts for
8 papers
Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning
Mustafa Safa Ozdayi, Charith Peris, Jack FitzGerald +5
Large Language Models (LLMs) are known to memorize significant portions of their training data. Parts of this memorized content have been shown to be extractable by simply querying…
Towards Realistic Single-Task Continuous Learning Research for NER
Justin Payan, Yuval Merhav, He Xie +4
There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications. Meanwhile, there is still a lack o…
An Efficient DP-SGD Mechanism for Large Scale NLP Models
Christophe Dupuy, Radhika Arava, Rahul Gupta +1
Recent advances in deep learning have drastically improved performance on many Natural Language Understanding (NLU) tasks. However, the data used to train NLU models may contain pr…
ADePT: Auto-encoder based Differentially Private Text Transformation
Satyapriya Krishna, Rahul Gupta, Christophe Dupuy
Privacy is an important concern when building statistical models on data containing personal information. Differential privacy offers a strong definition of privacy and can be used…
Joint Multi-Dimensional Model for Global and Time-Series Annotations
Anil Ramakrishna, Rahul Gupta, Shrikanth Narayanan
Crowdsourcing is a popular approach to collect annotations for unlabeled data instances. It involves collecting a large number of annotations from several, often naive untrained an…
A task in a suit and a tie: paraphrase generation with semantic augmentation
Su Wang, Rahul Gupta, Nancy Chang +1
Paraphrasing is rooted in semantics. We show the effectiveness of transformers (Vaswani et al. 2017) for paraphrase generation and further improvements by incorporating PropBank la…