Generative Transformer Model

Generative transformer models are powerful neural networks designed to generate various data types, including text, images, and even 3D structures, by learning patterns from large datasets. Current research focuses on improving their ability to handle long sequences, enhancing the quality and aesthetic appeal of generated outputs, and addressing challenges in out-of-distribution generalization and interpretability, often employing architectures like LLAMA and GPT models. These advancements have significant implications across diverse fields, enabling applications in drug discovery (de novo molecule design), visual analytics, and improving the capabilities of AI systems in tasks requiring reasoning and decision-making.