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A token sequence is a fundamental data structure in language modeling where a sentence is broken down into individual units, or tokens. Positional information is often included to indicate the order of the tokens. For example, the sentence 'The kitten is chasing the ball .' can be represented as a sequence of tokens with their positions marked by subscripts: 'The₁ kitten₂ is₃ chasing₄ the₅ ball₆ .'.

Example of a Token Sequence

Language models utilize special tokens that do not represent words from the vocabulary but instead provide structural or control information. For instance, the `⟨SOS⟩` (Start of Sequence) token marks the beginning of an input, while the `⟨pad⟩` token is used to equalize sequence lengths within a batch for efficient processing. These tokens are essential for managing the input and output data streams of the model.

Special Tokens in Language Models

A language model processes text by breaking it down into an ordered sequence of tokens. Arrange the following tokens to reconstruct the original sentence: 'The model predicts the next word .'

A language model processes text by breaking it into an ordered sequence of tokens, where each token is a unit of text with an associated position. Given the sentence 'The model learns patterns.', represent it as a sequence of tokens, marking the position of each token with a subscript number (e.g., `word₁`).

Representing Text as a Token Sequence

A language model processes text by breaking it into an ordered sequence of tokens, where each token is a unit of text (like a word or punctuation mark) with an associated position. Consider the following two sentences:

Sentence A: 'The fast car races.'
Sentence B: 'The fast cars race.'

Which of the following options most accurately represents the distinct token sequences for these two sentences as a typical tokenizer would produce them?

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