Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a
groundbreaking paradigm for intelligent edge devices. With their capacity for human-like …

Memorization, or the tendency of large language models (LLMs) to output entire sequences
from their training data verbatim, is a key concern for deploying language models. In …

Large language models (LMs) have been shown to memorize parts of their training data,
and when prompted appropriately, they will emit the memorized training data verbatim. This …

Existing foundation models are trained on copyrighted material. Deploying these models
can pose both legal and ethical risks when data creators fail to receive appropriate …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Differentially Private (DP) learning has seen limited success for building large deep learning
models of text, and straightforward attempts at applying Differentially Private Stochastic …

We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-
scale pre-trained language models, which achieve the state-of-the-art privacy versus utility …

Large language models (LLMs) are excellent in-context learners. However, the sensitivity of
data contained in prompts raises privacy concerns. Our work first shows that these concerns …

Natural language reflects our private lives and identities, making its privacy concerns as
broad as those of real life. Language models lack the ability to understand the context and …