Federated learning (FL) typically faces data heterogeneity, ie, distribution shifting among
clients. Sharing clients' information has shown great potentiality in mitigating data …

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 …

Recent developments in deep learning have led to great success in various natural
language processing (NLP) tasks. However, these applications may involve data that …

Large language models (LLMs) are complex artificial intelligence systems capable of
understanding, generating and translating human language. They learn language patterns …

The advancement of large language models (LLMs) has significantly enhanced the ability to
effectively tackle various downstream NLP tasks and unify these tasks into generative …

This is the first survey of the active area of AI research that focuses on privacy issues in
Large Language Models (LLMs). Specifically, we focus on work that red-teams models to …

Large Language Models (LLMs) represent a significant advancement in artificial
intelligence, finding applications across various domains. However, their reliance on …

We studied 53 privacy-preserving neural-network papers in 2016-2022 based on
cryptography (without trusted processors or differential privacy), 16 of which only use …

Service providers of large language model (LLM) applications collect user instructions in the
wild and use them in further aligning LLMs with users' intentions. These instructions, which …

Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a
wide range of general natural language processing (NLP) tasks. Researchers have …