The Transformer architecture introduced in 2017 revolutionized natural language processing. It consists of encoder and decoder blocks that process sequences in parallel using self-attention mechanisms, eliminating the need for recurrent connections and enabling much faster training.
Self-attention is the core innovation of Transformers. Each token can attend to every other token in the sequence, creating weighted connections based on relevance. This mechanism captures long-range dependencies that traditional RNNs struggle with, enabling parallel processing and better context understanding.
Large Language Models scale the Transformer architecture dramatically. BERT started with 340 million parameters, GPT-2 had 1.5 billion, GPT-3 reached 175 billion, and GPT-4 is estimated at 1.7 trillion parameters. This exponential scaling, combined with massive training datasets, enables emergent capabilities like few-shot learning and complex reasoning.
LLM training involves two main phases. First, pretraining on massive text corpora using next-token prediction teaches the model language patterns and world knowledge. Then fine-tuning adapts the model for specific tasks. This process enables remarkable capabilities including text generation, translation, code synthesis, question answering, and complex reasoning with minimal examples.
Despite remarkable progress, LLMs face significant challenges. Computational costs are enormous, requiring millions of dollars for training. Models can hallucinate false information, amplify biases from training data, and pose safety risks. Future research focuses on improving efficiency through smaller models, enhancing reasoning capabilities, better alignment with human values, and expanding multimodal integration.