Large language models (LLMs) rely on the transformer architecture, a sophisticated neural network that processes sequences of token embeddings to generate text. The process begins with tokenization, where raw text is divided into discrete tokens, which are then mapped to identifiers. These identifiers are used to create embedding vectors that carry semantic and lexical information. Positional encoding is added to these vectors to provide information about the position of each token within the sequence, preparing the input for the deeper layers of the transformer. Inside the transformer, each token embedding undergoes multiple transformations. The first major component is multi-headed attention, which enriches each token's representation by capturing various linguistic relationships within the text. This component is crucial for understanding the role of each token in the sequence. Following this, feed-forward neural network layers further refine the token features, applying transformations independently to each token. This process is repeated across multiple layers, progressively enhancing the token embeddings with more abstract and long-range linguistic information. At the final stage, the enriched token representation is processed through a linear output layer and a softmax function to produce next-token probabilities. The linear layer generates unnormalized scores, or logits, which the softmax function converts into normalized probabilities for each possible token in the vocabulary. The model then selects the next token to generate, typically the one with the highest probability. Understanding this journey from input tokens to output probabilities is crucial for comprehending how LLMs generate coherent and context-aware text. This matters because it provides insight into the inner workings of AI models that are increasingly integral to various applications in technology and communication.
