Large Pre Trained Transformer

Large pre-trained transformer models are revolutionizing various fields by leveraging their ability to learn complex patterns from massive datasets and then adapt to specific downstream tasks with relatively little additional training. Current research focuses on improving efficiency through techniques like parameter-efficient fine-tuning, model compression (including pruning and decomposition), and knowledge distillation, often employing architectures such as Vision Transformers (ViTs) and variations of BERT and GPT. These advancements are significantly impacting diverse applications, from financial time series analysis and medical image classification to program synthesis and multimodal learning, enabling more accurate and efficient solutions in data-rich environments.