Large language models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

Humans possess an extraordinary ability to create and utilize tools. With the advent of
foundation models, artificial intelligence systems have the potential to be equally adept in …

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 …

This paper reexamines the research on out-of-distribution (OOD) robustness in the field of
NLP. We find that the distribution shift settings in previous studies commonly lack adequate …

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 …

Abstract Instruction-tuned Large Language Models (LLMs) have become a ubiquitous
platform for open-ended applications due to their ability to modulate responses based on …

The huge training overhead, considerable commercial value, and various potential security
risks make it urgent to protect the intellectual property (IP) of Deep Neural Networks (DNNs) …

Recent studies have revealed that\textit {Backdoor Attacks} can threaten the safety of natural
language processing (NLP) models. Investigating the strategies of backdoor attacks will help …

Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …

In the field of natural language processing, the prevalent approach involves fine-tuning
pretrained language models (PLMs) using local samples. Recent research has exposed the …