[HTML][HTML] A survey on large language model (llm) security and privacy: The good, the bad, and the ugly
Abstract Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized
natural language understanding and generation. They possess deep language …
natural language understanding and generation. They possess deep language …
A comprehensive survey of forgetting in deep learning beyond continual learning
Forgetting refers to the loss or deterioration of previously acquired information or knowledge.
While the existing surveys on forgetting have primarily focused on continual learning …
While the existing surveys on forgetting have primarily focused on continual learning …
Investigating the catastrophic forgetting in multimodal large language models
Following the success of GPT4, there has been a surge in interest in multimodal large
language model (MLLM) research. This line of research focuses on developing general …
language model (MLLM) research. This line of research focuses on developing general …
Prompt as triggers for backdoor attack: Examining the vulnerability in language models
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 …
tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot …
A comprehensive study on robustness of image classification models: Benchmarking and rethinking
The robustness of deep neural networks is frequently compromised when faced with
adversarial examples, common corruptions, and distribution shifts, posing a significant …
adversarial examples, common corruptions, and distribution shifts, posing a significant …
Certified robustness against natural language attacks by causal intervention
Deep learning models have achieved great success in many fields, yet they are vulnerable
to adversarial examples. This paper follows a causal perspective to look into the adversarial …
to adversarial examples. This paper follows a causal perspective to look into the adversarial …
Flirt: Feedback loop in-context red teaming
Warning: this paper contains content that may be inappropriate or offensive. As generative
models become available for public use in various applications, testing and analyzing …
models become available for public use in various applications, testing and analyzing …
Pre-trained adversarial perturbations
Self-supervised pre-training has drawn increasing attention in recent years due to its
superior performance on numerous downstream tasks after fine-tuning. However, it is well …
superior performance on numerous downstream tasks after fine-tuning. However, it is well …
Robustness analysis of video-language models against visual and language perturbations
Joint visual and language modeling on large-scale datasets has recently shown good
progress in multi-modal tasks when compared to single modal learning. However …
progress in multi-modal tasks when compared to single modal learning. However …
Defending against weight-poisoning backdoor attacks for parameter-efficient fine-tuning
Recently, various parameter-efficient fine-tuning (PEFT) strategies for application to
language models have been proposed and successfully implemented. However, this raises …
language models have been proposed and successfully implemented. However, this raises …