Watch: Language Model
A model trained to understand and generate text by learning the statistical patterns of language — the foundational class of model underpinning search, AI assistants, and every DEJAN classifier.
Transcript
At its core, a language model is a system trained on massive amounts of text to learn the statistical structure of human language. It figures out which words are likely to follow one another, how context shapes meaning, and how sentences are built. Modern language models use transformer architectures and fall into two main categories depending on their goals. Generative models complete or continue text, while discriminative models analyze and rank it.
These models come in all sizes. Small, specialized models might have tens of millions of parameters and run on a single graphics card. Large Language Models, or LLMs, scale up to billions or even trillions of parameters, requiring massive computing networks. In the middle are distilled models, which are shrunk down to be more efficient while keeping most of their original capabilities.
For anyone working in search engine optimization, understanding these models is now essential. Today's search engines, AI answer assistants, and recommendation platforms are either language models themselves or heavily driven by them. How these models calculate probabilities, store associations, and retrieve knowledge directly shapes how content is found and ranked. Understanding the mechanics of language models is the new foundation for visibility in an AI-driven web.