What happens during the pre-training of a large language model?

What happens during the pre-training of a large language model?

Pre-training is an initial stage in which a model learns broad patterns from a large collection of data. For an autoregressive language model, a common objective is predicting the next token from the tokens that precede it.

The resulting model can acquire useful language and task patterns before being shaped for a particular assistant experience.

Preparing prediction examples

Text is collected, processed, and converted into tokens. Data sources can include web pages, books, code, licensed collections, and other material, depending on the project.

A sequence such as “Water freezes at zero degrees Celsius” supplies multiple prediction positions. The model might receive an earlier part of the sequence and assign probabilities to possible next tokens. Across training, it encounters many such contexts.

Updating the model at scale

The model performs forward computations, calculates a loss against target tokens, and uses backpropagation and an optimizer to adjust parameters. Repeated updates encourage patterns that improve prediction across the data mixture.

This objective can support grammar, factual associations, coding patterns, and some problem-solving abilities. The GPT-3 paper describes how a pre-trained model can perform tasks from instructions and examples in context.

What pre-training does not guarantee

Predicting data well does not automatically produce a reliable assistant. The data may contain contradictions, outdated statements, and undesirable behavior.

Further training, prompting, retrieval, tools, and evaluation can help shape deployment. The model also does not become a searchable copy of every training document or a source of guaranteed factual answers.