Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

This article surveys and organizes research works in a new paradigm in natural language
processing, which we dub “prompt-based learning.” Unlike traditional supervised learning …

Large language models (LLMs) are gaining increasing popularity in both academia and
industry, owing to their unprecedented performance in various applications. As LLMs …

Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

Pretraining large neural language models, such as BERT, has led to impressive gains on
many natural language processing (NLP) tasks. However, most pretraining efforts focus on …

Neurodevelopmental Disorders (NDD) are a group of conditions with onset in the
developmental period characterized by deficits in the cognitive and social areas …

Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …

Human language is often multimodal, which comprehends a mixture of natural language,
facial gestures, and acoustic behaviors. However, two major challenges in modeling such …