The Transformer is a groundbreaking neural network architecture introduced in 2017. Unlike traditional models that process sequences step by step, Transformers use attention mechanisms to analyze all parts of the input simultaneously. This parallel processing makes them incredibly efficient and powerful for language understanding tasks.
Self-attention is the core innovation of Transformers. For each word in the input sequence, the model creates three vectors: Query, Key, and Value. The attention mechanism computes how much each word should attend to every other word in the sequence. This allows the model to capture long-range dependencies and contextual relationships between words, regardless of their distance in the sequence.
Multi-head attention is a key enhancement that allows Transformers to capture different types of relationships simultaneously. Instead of using a single attention mechanism, the model runs multiple attention heads in parallel. Each head focuses on different aspects of the input, such as syntactic relationships, semantic meanings, or positional information. The outputs from all heads are then concatenated and linearly transformed to produce the final representation.
Text generation in Transformers follows an autoregressive process. Starting with a prompt, the model predicts the probability distribution over all possible next tokens. The decoder uses masked self-attention to ensure it only considers previous tokens, not future ones. Once a token is selected, it's added to the sequence and the process repeats. This iterative approach allows Transformers to generate coherent, contextually appropriate text one token at a time.