Tokenización de LLM
N/AconceptAlso known as: Model tokenisation; subword tokenisation; tokenizer encoding
La tokenización de LLM es la conversión de texto u otra entrada en identificadores de tokens específicos del modelo que un modelo de lenguaje grande puede procesar.
Technical explanation
A tokenizer normalises and segments input into words, subwords, characters or bytes, maps segments to vocabulary IDs and adds special tokens. Padding and truncation shape batches; decoding maps generated IDs back to text.
Business relevance
Tokenisation affects context usage, latency, cost, multilingual behaviour, input limits and how reliably specialised terms, code or identifiers are represented.
Implementation example
Before sending long documents to an LLM, a pipeline counts tokens with the model's own tokenizer, preserves required instructions and chunks source text within the context limit.
Limitations and common misconceptions
Tokens are not equivalent to words or meaning, and counts vary by model and language. Truncation can remove critical context, while poor segmentation can disadvantage scripts, domains or uncommon strings.
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