Estimating the semantic similarity between text data is one of the challenging and open
research problems in the field of Natural Language Processing (NLP). The versatility of …

Along with the springing up of the semantics-empowered communication (SemCom)
research, it is now witnessing an unprecedentedly growing interest towards a wide range of …

Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and
clinical application, and effectively identifying potential D-DIs during clinical trials is critical …

Accurately predicting drug-target binding affinity (DTA) in silico is a key task in drug
discovery. Most of the conventional DTA prediction methods are simulation-based, which …

Generating realistic images from text descriptions is a challenging problem in computer
vision. Although previous works have shown remarkable progress, guaranteeing semantic …

Sentence similarity modeling lies at the core of many natural language processing
applications, and thus has received much attention. Owing to the success of word …

In the era of large language models (LLMs), in-context learning (ICL) stands out as an
effective prompting strategy that explores LLMs' potency across various tasks. However …

Automatic charge prediction aims to predict appropriate final charges according to the fact
descriptions for a given criminal case. Automatic charge prediction plays a critical role in …

A drug-drug interaction (DDI) occurs when a drug is combined with other drug (s). DDIs have
the potential to obstruct, increase, or diminish the intended impact of a drug or, in the worst …

The similarity between two sentences is the score that represents the relatedness and
likelihood between those sentences. Measuring sentence similarity has attracted …