You are setting up a preprocessing pipeline for a transformer-based Large Language Model (LLM). What is the purpose of the initial "tokenization" step?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Before a computer can make heads or tails of a sentence, it has to break it down into smaller, bite-sized pieces. Think of it like taking a long network packet and chopping it into individual frames. In NLP, this process is called tokenization. You take a continuous block of text and slice it up into "tokens"—which could be whole words, subwords, or even individual characters. It's the absolute first step in any NLP pipeline. Don't confuse it with embedding, which is converting those words into numbers, or cleaning, which is stripping out useless words!
Full explanation below image
![A diagram illustrating tokenization. A text block containing the sentence "Network engineers love automation!" is shown passing through a "Tokenizer" block, which outputs a list of separate, highlighted boxes (tokens): ["Network", "engineers", "love", "automation", "!"].](/media/blockchain-council/caid/CAID-q047-infographic.webp)
Full Explanation
Tokenization is the foundational preprocessing step in Natural Language Processing (NLP). It is the process of splitting a continuous sequence of characters (a text string) into smaller, meaningful units called tokens. Depending on the design of the tokenizer, these tokens can be: - Words: Splitting text by whitespace and punctuation (e.g., "Let's go" -> ["Let", "'", "s", "go"]). - Subwords: Breaking words into common sub-components (e.g., "unbelievable" -> ["un", "believ", "able"]), which is highly effective for handling out-of-vocabulary words in modern models like BERT or GPT. - Characters: Slicing the text into individual letters or symbols.
Without tokenization, a model would treat an entire paragraph as a single, massive string, making text analysis impossible.
- Option B is incorrect because removing common, low-information words (like "the", "is", "at") is called "stop word removal," which is a separate text cleaning step. - Option C is incorrect because converting text or tokens into numerical vector representations is known as "word embedding" or "vectorization" (e.g., Word2Vec, TF-IDF). - Option D is incorrect because correcting spelling mistakes is part of text cleaning, normalization, or spell-checking, not tokenization.