Discover the key distinction between tokens and words in natural language processing (NLP). Learn how language models break down text into meaningful units called tokens.
Table of Contents
Question
How is a token different than a word?
A. A token represents a full sentence, but a word does not.
B. A token is always longer than a word.
C. A token is a unit a language model understands, and a word can consist of multiple tokens.
D. A token is used in natural language processing, while a word is used in coding.
Answer
C. A token is a unit a language model understands, and a word can consist of multiple tokens.
Explanation
In natural language processing (NLP), a token is the smallest meaningful unit of text that a language model can understand and process. While words are the basic units of meaning in human language, they are not always the same as tokens in NLP.
Here’s why:
- Tokenization: NLP models break down text into tokens through a process called tokenization. This process can split words into smaller units or group words together based on the model’s understanding of the language.
- Subwords: Some words, especially long or complex ones, can be broken down into multiple tokens. For example, the word “unbelievable” might be tokenized as [“un”, “believe”, “able”] by the model. Each of these tokens contributes to the overall meaning of the word.
- Punctuation and special characters: Punctuation marks and special characters are often treated as separate tokens. For instance, “don’t” could be tokenized as [“do”, “n’t”], and “example.com” might become [“example”, “.”, “com”].
- Context-dependent tokens: Some language models, like GPT, use tokens that are specific to the training data and the model’s architecture. These tokens might not always correspond directly to individual words but are optimized for the model’s performance.
So, while words are the basic semantic units we use in human language, tokens are the fundamental units that language models use to process, understand, and generate text. A single word can be represented by multiple tokens, depending on the model and the specific context.
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