Learn what tokens are and how they are used to convert natural language text into numerical representation for large language models.
Single Choice Question
In Large Language Models (LLMs), the input text is divided into pieces and converted into numeric values. What is the term used to describe each of these input chunks?
B. Embedding Function
Large Language Models (LLMs) are machine learning models that can process and generate natural language text, such as sentences, paragraphs, or documents. LLMs are trained on a large amount of text data, and learn to predict the next word or words in a sequence, given some previous words or context.
However, LLMs cannot directly work with raw text, as they need a numerical representation of the input and output. Therefore, the input text is first split up into words or word parts, called tokens, and a numerical representation of these are produced. So we started with natural language text, but now we have a lot of numbers that encode useful information, learned during training, about each word or word part in context.
A token can be a whole word, a subword, or a character, depending on the tokenization method used. Tokenization is the process of dividing the text into tokens, and assigning a unique numerical identifier to each token. For example, the sentence “I love cats” can be tokenized into three tokens: “I”, “love”, and “cats”. Each token can then be mapped to a number, such as 1, 2, and 3, respectively.
The numerical representation of the tokens is then fed into the LLM, which uses a neural network architecture, such as a Transformer, to process the input and generate the output. The output is also a sequence of numbers, which can then be converted back to text using the inverse mapping of the tokenization.
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