Large Language Model (LLM)
A large language model is an AI system trained on massive text datasets to understand and generate human language. GPT-4, Claude, and Gemini are examples.
Definition¶
A large language model (LLM) is a type of artificial intelligence trained on vast quantities of text data to predict and generate language. "Large" refers to both the training data (often trillions of tokens) and the parameter count (billions to hundreds of billions of weights).
How LLMs Work¶
LLMs are built on the transformer architecture and trained through a process called self-supervised learning: the model reads text, predicts what comes next, compares its prediction to the actual next token, and adjusts its weights accordingly — across billions of training examples.
The result is a model that has implicitly learned grammar, factual associations, reasoning patterns, and writing style from the text it was trained on. It doesn't "understand" in a human sense, but it generates statistically coherent and often accurate text.
Major LLMs in Use (2026)¶
| Model | Organization |
|---|---|
| GPT-4 / o3 | OpenAI |
| Claude 3.5 / Claude 4 | Anthropic |
| Gemini 2.5 | Google DeepMind |
| Llama 3 | Meta |
| Mistral | Mistral AI |
LLMs and Text Humanization¶
Text generated by LLMs carries statistical fingerprints from their training process — patterns that make AI-generated text identifiable to both humans and detection tools. These fingerprints are what AI humanization tools work to remove or obscure.
Humanization works by introducing variation that breaks the LLM's characteristic patterns: more varied sentence structure, less predictable word choices, and natural-sounding idiosyncrasies that aren't common in model output.