Abstract Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized
natural language understanding and generation. They possess deep language …

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 …

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 …

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 …

The robustness of deep neural networks is frequently compromised when faced with
adversarial examples, common corruptions, and distribution shifts, posing a significant …

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 …

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 …

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 …

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 …

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