Which statement best describes how an LLM generates text?

The choice that accurately describes how a Large Language Model (LLM) generates text is the one that states it generates text by repeatedly predicting the next word. This process involves the model using its training on vast amounts of text data to understand the patterns and probabilities of word sequences. When a user inputs a prompt, the LLM analyzes that context and generates the most probable next word based on the input and its learned knowledge.

This word-by-word generation continues, with each new word potentially influencing the prediction of subsequent words, ensuring that the completed text remains coherent and relevant to the given prompt. The model does not create sentences in random order or depend on human collaboration or supervised learning in the specific context of text generation. Instead, the mechanism is a statistical prediction based on previously learned language patterns, making the process inherently systematic rather than haphazard or collaborative.