Large language models, explained [Lee]

[mklabgr]

Large language models, explained
by  [Lee]

Summary

The article explains large language models (LLMs) in a clear, non-technical way by showing that they work by learning statistical patterns in vast amounts of text and using them to predict the next word (or token) in a sequence. It describes how models like GPT are built on transformer neural networks that process language step by step through many layers, gradually refining word meanings based on context. 
The post emphasizes that LLMs do not explicitly “understand” language like humans do, but instead represent words as numerical vectors that capture contextual relationships, allowing them to generate coherent responses, translations, and other language tasks. 
It also highlights that much of their impressive ability comes from scale—massive datasets and huge computational resources—rather than handcrafted rules, and that their internal workings are still not fully understood by researchers.

ARTICLE