The neural networks at the heart of language models are mathematical structures loosely inspired by the human brain. Each one contains many artificial neurons arranged in layers, with connections between neurons in adjacent layers. The neural network\u2019s behavior is governed by the strength of these connections, called parameters. In a language model, the parameters control which words the model might spit out next, given an initial prompt and the words it has generated already.<\/p>\n

A model only truly comes to life during training, when it repeatedly compares its own output to the text in its training data set and adjusts its parameters to increase the resemblance. An untrained network with random parameters is trivially easy to assemble from a few lines of code, but it will just produce gibberish. After training, it can often plausibly continue unfamiliar text. Larger models often undergo further fine-tuning that teaches them to answer questions and follow instructions, but the bulk of the training is mastering word prediction.<\/p>\n