How Transformer Decoders Generate Text — From Causal Masking to Decoding

Transformer Decoders generate text by predicting one token at a time, feeding each newly generated token back into the model to predict the next, which is fundamental to modern language model (LLM) generation. This autoregressive process ensures that each token is generated based on the context provided by all previous tokens, enabling coherent and contextually relevant text production. Understanding how these decoders work provides insight into the mechanics behind sophisticated language generation, which has significant implications for applications ranging from automated writing to advanced chatbots.