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The basic unit of text that a language model processes, typically representing a word, subword, or character. Tokens are the fundamental building blocks for LLM input and output.

A collection of data examples used for training, validating, or testing machine learning models.

A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

Learning useful features or representations of data automatically, rather than hand-crafting them.

A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together.

A technique that lets a neural network weigh every part of its input when producing each output, focusing on the parts most relevant at that step.

A neural network architecture, introduced in 2017, built from stacked self-attention and feed-forward layers; the basis of nearly every modern large language model.

The maximum number of tokens an LLM can process at once, including both input prompt and generated output. Also called context length.