Applicating third-party data and models has become a new paradigm for language modeling
in NLP, which also introduces some potential security vulnerabilities because attackers can …

Large Language Models (LLMs), which bridge the gap between human language
understanding and complex problem-solving, achieve state-of-the-art performance on …

Recently, various parameter-efficient fine-tuning (PEFT) strategies for application to
language models have been proposed and successfully implemented. However, this raises …

Increasingly, model compression techniques enable large language models (LLMs) to be
deployed in real-world applications. As a result of this momentum towards local deployment …

Despite being widely applied due to their exceptional capabilities, Large Language Models
(LLMs) have been proven to be vulnerable to backdoor attacks. These attacks introduce …

Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of pre-trained language
models (PLMs) to specific tasks. By tuning only a minimal set of (extra) parameters, PEFT …

Despite being widely applied, pre-trained language models have been proven vulnerable to
backdoor attacks. Backdoor attacks are designed to introduce targeted vulnerabilities into …

In this study, we introduce SpamDam, a SMS spam detection framework designed to
overcome key challenges in detecting and understanding SMS spam, such as the lack of …

Backdoor attacks pose a significant threat to neural networks, enabling adversaries to
manipulate model outputs on specific inputs, often with devastating consequences …

With the growing demand for personalized AI solutions, customized LLMs have become a
preferred choice for businesses and individuals, driving the deployment of millions of AI …