This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

With the urgent demand for generalized deep models, many pre-trained big models are
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

The contemporary LLMs are prone to producing hallucinations, stemming mainly from the
knowledge gaps within the models. To address this critical limitation, researchers employ …

Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

The new generation of state-of-the-art computer vision systems are trained from natural
language supervision, ranging from simple object category names to descriptive captions …

Large Language Models (LLMs) have garnered considerable interest within both academic
and industrial. Yet, the application of LLMs to graph data remains under-explored. In this …

Abstract Knowledge plays a critical role in artificial intelligence. Recently, the extensive
success of pre-trained language models (PLMs) has raised significant attention about how …

Understanding rich narratives, such as dialogues and stories, often requires natural
language processing systems to access relevant knowledge from commonsense knowledge …