Large language models (LLMs) are increasingly being used for generating text in a variety
of use cases, including journalistic news articles. Given the potential malicious nature in …

Hierarchical text classification (HTC) is an important task with broad applications, and few-
shot HTC has gained increasing interest recently. While in-context learning (ICL) with large …

Prompt tuning for pre-trained masked language models (MLM) has shown promising
performance in natural language processing tasks with few labeled examples. It tunes a …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Prompting method is regarded as one of the crucial progress for few-shot nature language
processing. Recent research on prompting moves from discrete tokens based``hard …

Understanding the subcellular localization of long non-coding RNAs (IncRNAs) is crucial for
unraveling their functional mechanisms. While previous computational methods have made …

At present, image classification covers more and more fields, and it is often difficult to obtain
enough data for learning in some specific scenarios, such as medical fields, personalized …

Distribution estimation has been demonstrated as one of the most effective approaches in
dealing with few-shot image classification, as the low-level patterns and underlying …

Through minimization of an appropriate loss function such as the InfoNCE loss, contrastive
learning (CL) learns a useful representation function by pulling positive samples close to …

Many problems related to natural language processing are solved by neural networks and
big data. Researchers have previously focused on single-task supervised goals with limited …