What is a token in the context of a large language model (LLM)?

In the context of a large language model (LLM), a token refers to a unit of text that can represent a word, part of a word, or even punctuation. This means that when the LLM processes input text or generates output, it does so in terms of these tokens, breaking down the text into manageable pieces for understanding and generation. The model's architecture is designed around the idea of manipulating these tokens to understand context, meaning, and relationships in language.

The definition of a token being a word or part of a word highlights how LLMs handle diverse linguistic inputs, which might include common words, compound words, or specialized terminology. When generating output, the model will combine these tokens to form coherent responses based on learned patterns and probabilities derived from training data.

The other options introduce concepts that do not align with the definition of a token within the LLM framework; for instance, tokens are not about symbolic value, cryptocurrency, or user authentication, but rather represent the fundamental building blocks of language processed by the model.