Generative Pre-trained Transformer

Generative Pre-trained Transformer (GPT) is a type of large language model architecture that uses transformer neural networks to generate human-like text by predicting the next word in a sequence. The model undergoes pre-training on massive datasets of text from the internet, learning patterns in language, syntax, semantics, and world knowledge without task-specific supervision. This pre-training phase enables the model to develop general language understanding capabilities that can be fine-tuned or adapted for specific applications through techniques like prompt engineering or few-shot learning. GPT models utilize self-attention mechanisms to process input sequences in parallel, allowing them to capture long-range dependencies and contextual relationships effectively. The architecture consists of multiple transformer decoder layers that generate text autoregressively, producing one token at a time based on previous context. Modern GPT implementations like GPT-3 and GPT-4 demonstrate emergent capabilities including reasoning, code generation, mathematical problem-solving, and creative writing, making them valuable for enterprise applications ranging from content creation to automated customer support and intelligent document processing.