Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Recently, the emergence of pre-trained models (PTMs) has brought natural language
processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs …

The increasing scale of large language models (LLMs) brings emergent abilities to various
complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is …

Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment.
Writing imaging reports is time-consuming and can be error-prone for inexperienced …

While some studies have proven that Swin Transformer (Swin) with window self-attention
(WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad …

In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a
novel Chinese pre-trained unbalanced transformer (CPT). Different from previous Chinese …

Abstract Invariant Risk Minimization (IRM) is an emerging invariant feature extracting
technique to help generalization with distributional shift. However, we find that there exists a …

Chain-of-thought prompting (CoT) advances the reasoning abilities of large language
models (LLMs) and achieves superior performance in arithmetic, commonsense, and …

Artificial Intelligence (AI), along with the recent progress in biomedical language
understanding, is gradually changing medical practice. With the development of biomedical …

Lexicon information and pre-trained models, such as BERT, have been combined to explore
Chinese sequence labelling tasks due to their respective strengths. However, existing …