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Tokenization

Turkish equivalent: TokenleştirmeDomain: Large Language Models

The conversion of input text or other content into discrete model units whose integer identifiers are processed by a neural network.

Language-Model Context

Tokenization converts input into the discrete units and integer identifiers processed by a model. Subword methods reduce out-of-vocabulary problems while controlling vocabulary size, but token efficiency differs across languages and directly affects context consumption and inference cost.

Representation Boundary

A token is not the same as a word. One word can span several tokens, while one token can represent several characters, bytes, punctuation marks, or a complete short word.

Related technical article: Artificial Intelligence: Philosophy, Theory and Practice.

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Related concepts

Large Language Models

Related technical publications

Publications whose title or summary directly references this concept.

Natural Language Processing

A Turkish-centered NLP course covering Unicode and normalization, tokenization, morphology, syntax, semantics, statistical NLP, Transformers, multilingual models, speech, LLMs, and production engineering in a comparative language perspective.