Token Selection from Probability Distribution

Step-by-Step Example of Auto-Regressive Sequence Generation

Mathematical Formulation of Draft Model Prediction in Speculative Decoding

Iterative Context Update in Autoregressive Generation

Key-Value (KV) Cache in Transformer Inference

Sequential Generation of Output Tokens

Context Shifting in Auto-Regressive Generation

A language model is generating a sentence and has so far produced the sequence: ['The', 'cat', 'sat']. Based on the principles of sequential, one-at-a-time token generation where each new token depends on the ones before it, what is the direct input the model will use to determine the next token in the sequence?

A language model generates text by producing a single token at each step, using the entire sequence generated so far as the context for the next token. Arrange the following events in the correct chronological order to illustrate the generation of two new tokens following the initial input 'The ocean is'.

A researcher develops a novel text generation model. Given an input like 'The movie was', instead of generating one token at a time, this model predicts the entire completion (e.g., 'incredibly boring and predictable') in a single, parallel step. Which core principle of the standard auto-regressive process is fundamentally violated by this new model's design?