In language models, what are tokens?

Tokens in language models refer to the individual pieces that make up the input text, which can include parts of words, entire words, or punctuation marks. This flexible definition is crucial for how language models interpret and process text. By breaking the text into manageable units, the model can better understand context, semantics, and structure.

For instance, a single word may be tokenized as an entire unit, but compound words or words with prefixes and suffixes might be split into smaller segments, and punctuation is treated as standalone tokens. This approach allows models to capture a wide range of linguistic features and adapt to different languages and textual structures effectively, enhancing their ability to generate and predict language comprehensively.

In contrast, the other options provide limited or overly specific definitions, such as only considering entire sentences, exclusively focusing on punctuation, or identifying a single word in a dataset, which do not encompass the broader and more nuanced understanding of what tokens can be in the context of language modeling.