We investigate security concerns of the emergent instruction tuning paradigm, that models
are trained on crowdsourced datasets with task instructions to achieve superior …

Backdoor defenses have been studied to alleviate the threat of deep neural networks
(DNNs) being backdoor attacked and thus maliciously altered. Since DNNs usually adopt …

The prompt-based learning paradigm, which bridges the gap between pre-training and fine-
tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot …

Although backdoor learning is an active research topic in the NLP domain, the literature
lacks studies that systematically categorize and summarize backdoor attacks and defenses …

The prompt-based learning paradigm has gained much research attention recently. It has
achieved state-of-the-art performance on several NLP tasks, especially in the few-shot …

Textual backdoor attacks are a kind of practical threat to NLP systems. By injecting a
backdoor in the training phase, the adversary could control model predictions via predefined …

Deep learning is becoming increasingly popular in real-life applications, especially in
natural language processing (NLP). Users often choose training outsourcing or adopt third …

Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, which poison the training
set to alter the model prediction over samples with a specific trigger. While existing efforts …

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 …

In the software engineering (SE) community, deep learning (DL) has recently been applied
to many source code processing tasks, achieving state-of-the-art results. Due to the poor …