Autoregressive Language Model

An autoregressive language model is a neural system that builds up written output one token after another, always conditioning the next choice on everything it has already generated. The model learns to estimate how likely each possible next word or sub‑word piece is given the full history of prior tokens, and then draws from that distribution to continue the sequence. Because the generation proceeds step by step, the same architecture can be used both for predicting a missing continuation in an unfinished sentence and for creating entirely new paragraphs from scratch.

The importance of this approach lies in its ability to capture the natural flow of language: it respects grammar, coherence, and context without needing external rules. Autoregressive models power many everyday applications such as predictive keyboards, chat assistants, story‑writing tools, and machine translation services that render one language into another by producing a sequence of target words conditioned on the source text. Anywhere a system must produce human‑like text in real time, from drafting emails to answering questions, you are likely encountering an autoregressive language model at work.