In the era of deep learning, modeling for most natural language processing (NLP) tasks has
converged into several mainstream paradigms. For example, we usually adopt the …

Sentiment analysis is one of the traditional well-known tasks in Natural Language
Processing (NLP) research. In recent years, Pre-trained Models (PMs) have become one of …

The utilization of large language models (LLMs) for Healthcare has generated both
excitement and concern due to their ability to effectively respond to free-text queries with …

Prompt tuning, which only tunes continuous prompts with a frozen language model,
substantially reduces per-task storage and memory usage at training. However, in the …

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …

Sentiment analysis is a process in Natural Language Processing that involves detecting and
classifying emotions in texts. The emotion is focused on a specific thing, an object, an …

Aspect-based sentiment analysis (ABSA) endeavours predict the sentiment polarity of
specific aspects of a given review. Recently, prompt tuning has been widely explored and …

Self-supervised protein language models have proved their effectiveness in learning the
proteins representations. With the increasing computational power, current protein language …

Pragmatics, situated in the domains of linguistics and computational linguistics, explores the
influence of context on language interpretation, extending beyond the literal meaning of …

Prompt-tuning was proposed to bridge the gap between pretraining and downstream tasks,
and it has achieved promising results in Relation Extraction (RE). Although the existing …