Transformer Based Generative

Transformer-based generative models are revolutionizing various fields by leveraging the power of attention mechanisms to generate diverse data types, ranging from text and images to molecules and time series. Current research emphasizes improving model efficiency (e.g., through lightweight architectures) and addressing challenges like hallucination and interpretability, often employing autoregressive generation strategies and incorporating techniques like masked modeling or expert consultation networks. These advancements are significantly impacting diverse applications, including medical diagnosis, drug discovery, weather forecasting, and robotics, by enabling more accurate predictions, efficient data synthesis, and improved decision-making processes.