Artificial intelligence has taken great leaps recently, with one advance being generative language models that can write surprisingly human-like text. But how do these LLMs actually work? They don’t just parrot passages from their training data – they create original content by learning linguistic patterns. This article will describe the technology behind LLMs.
Teaching AI Models Language
The first step in developing LLMs is showing them huge datasets of text during training so they learn the fundamentals of human language. Models analyze the structure and organization behind hundreds of billions of words – detecting patterns in grammar, coherence, topics, and more.
LLMs use neural networks to absorb and process all this data. Information flows through interconnected nodes called neurons that perform computations to detect patterns. Billions of parameters within these networks capture all the nuances and regularities in flowing from word to word and topic to topic.
Forming New Connections
After training, an LLM has learned associations between words, concepts, and contexts. To generate text, the model starts with a prompt or given context. It analyzes this seed content to infer suitable succeeding words. The neural network then predicts coherent continuations word by word based on learned conventions and topic patterns.
Each next word stems from model confidence scores calculated by neurons assessing how well it fits given the built chain. Like a game of linguistic go-fish, the model chains related concepts and contexts into just the right hand. Paragraph after original paragraph emerges from these links formed by the model itself.
In short, given a certain context, the AI is good at predicting what each next word should be. With enough training data to deeply ingest patterns, LLMs can endlessly produce logically flowing text. LLMs demonstrate how AI can build upon previous training to accomplish novel tasks. As models grow bigger, so too will machine creativity.