During an AI literacy briefing, a colleague asks what people mean when they call a system a large language model. Which description best captures what a large language model is?
- AA model with very many parameters, trained on large amounts of text, that predicts likely token sequences to generate language. Correct
- BA program that stores a fixed dictionary of written phrases and returns the closest stored phrase to a query by exact keyword lookup.
- CAny model that processes images and video at high resolution, no matter whether it works with written or spoken language at all.
- DA rule-based engine in which engineers hand-code grammar rules so that every sentence the system writes follows author-written instructions.
Why A is correct: A large language model has a large parameter count learned from extensive text corpora and generates language by predicting probable token sequences, which is exactly what the term denotes.
Why B is wrong: Keyword lookup resembles older retrieval tools and is tempting, but a large language model generates text from learned patterns rather than returning stored phrases verbatim.
Why C is wrong: Handling images is tempting because large models are powerful, but a large language model is defined by working with language; pure image processing is a different capability.
Why D is wrong: Hand-coded grammar describes earlier symbolic systems and is a plausible confusion, but a large language model learns statistical patterns from data rather than executing authored rules.