Generative Pre-trained Transformers

Generative pre-trained transformers are neural network architectures that generate human-like text by predicting the next word in a sequence based on learned patterns from vast text corpora. These models undergo unsupervised pre-training on billions of tokens, learning language structure, grammar, and factual knowledge without explicit supervision. The transformer architecture employs self-attention mechanisms to process input sequences in parallel, capturing long-range dependencies and contextual relationships effectively. Pre-training involves next-token prediction objectives, enabling models to understand and generate coherent text across diverse domains. Following pre-training, these models can be fine-tuned for specific tasks through supervised learning or reinforcement learning from human feedback. Popular implementations include GPT series, PaLM, and LLaMA models. For AI agents, generative pre-trained transformers serve as reasoning engines, enabling natural language understanding, instruction following, and complex problem-solving capabilities essential for autonomous decision-making and human-AI interaction.