Abstract Generative Pre-trained Transformer (GPT) models have exhibited exciting progress
in their capabilities, capturing the interest of practitioners and the public alike. Yet, while the …

With the rapid development of artificial intelligence, large language models (LLMs) have
made remarkable advancements in natural language processing. These models are trained …

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

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 …

Large Language Models (LLMs) have become integral to numerous domains, significantly
advancing applications in data management, mining, and analysis. Their profound …

Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have
recently demonstrated exceptional capabilities for generating high-quality content. However …

Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …

We study the problem of in-context learning (ICL) with large language models (LLMs) on
private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the …

Large Language Models (LLMs) have emerged as dominant tools for various tasks,
particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns …

The rapid development of language models (LMs) brings unprecedented accessibility and
usage for both models and users. On the one hand, powerful LMs achieve state-of-the-art …