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In Natural Language Processing, tokens are the fundamental units of text created through a process called tokenization. While the terms 'token' and 'word' are often used interchangeably for simplicity, they have distinct meanings, as tokens are the basic building blocks that models process and may not always correspond directly to words.

Tokens vs. Words in NLP

A developer is testing a language model on a sentiment analysis task. The model correctly identifies 'This movie was fantastic!' as having positive sentiment. However, when given the input 'This movie was fantabulous!', the model returns a neutral or slightly negative sentiment, despite 'fantabulous' being a strongly positive word. Based on the fundamental way models process text, what is the most likely reason for this error?

Analyzing Unexpected Model Behavior

In the context of processing text with a large language model, explain why the number of 'tokens' in a sentence is often different from the number of 'words'. Provide a simple example to illustrate your explanation.

Explaining the Distinction Between Words and Tokens

A language model needs to process the sentence: 'Unforeseen challenges can't be ignored.' The model breaks the sentence into a sequence of fundamental units for processing. Which of the following sequences best demonstrates the principle that these units are often different from simple, space-separated words?

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