With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning
paradigm, it has been continuously shown that larger models tend to yield better …

Extremely large pre-trained language models (PTMs) such as GPT-3 are usually released
as a service. It allows users to design task-specific prompts to query the PTMs through some …

Prompt-learning has become a new paradigm in modern natural language processing,
which directly adapts pre-trained language models (PLMs) to $ cloze $-style prediction …

Recently," pre-training and fine-tuning''has been adopted as a standard workflow for many
graph tasks since it can take general graph knowledge to relieve the lack of graph …

Fine-tuning pre-trained large language models in a parameter-efficient manner is widely
studied for its effectiveness and efficiency. The popular method of low-rank adaptation …

Current pre-trained language models (PLM) are typically trained with static data, ignoring
that in real-world scenarios, streaming data of various sources may continuously grow. This …

Since real-world machine systems are running in non-stationary environments, Continual
Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually …

Soft prompt tuning achieves superior performances across a wide range of few-shot tasks.
However, the performances of prompt tuning can be highly sensitive to the initialization of …

Recent works on personalized text-to-image generation usually learn to bind a special token
with specific subjects or styles of a few given images by tuning its embedding through …

Despite the great success of pre-trained language models (PLMs) in a large set of natural
language processing (NLP) tasks, there has been a growing concern about their security in …