Generative Pretrained Transformer

Generative Pretrained Transformer (GPT) is a neural network architecture that uses transformer mechanisms to generate human-like text by predicting the next word in a sequence based on previous context. This foundational model architecture employs self-attention mechanisms to process and understand relationships between words across long sequences, enabling sophisticated language comprehension and generation capabilities. GPT models undergo extensive pretraining on vast text corpora to learn general language patterns, grammar, and knowledge before being fine-tuned for specific tasks. The transformer architecture’s parallel processing capabilities and attention mechanisms allow GPT models to capture complex linguistic dependencies and contextual nuances effectively. These models demonstrate emergent abilities in reasoning, code generation, creative writing, and problem-solving as they scale in size and training data. GPT architectures power numerous AI applications including chatbots, content creation tools, code assistants, and automated writing systems, representing a breakthrough in natural language processing technology.