LLMs
Token
In the context of AI, particularly Natural Language Processing (NLP) and Large Language Models (LLMs), a token is the smallest unit of text that a model processes. Tokens are used to convert raw text into a numerical representation that the model can understand and manipulate.
Explanation
Tokenization is the process of breaking down a sequence of text into individual tokens. These tokens can be words, parts of words (subwords), or even individual characters, depending on the tokenization algorithm used. Common tokenization methods include whitespace tokenization (splitting text by spaces), rule-based tokenization (using predefined rules to split text), and subword tokenization (breaking words into smaller, more frequent units). The choice of tokenization method significantly impacts the vocabulary size and the model's ability to handle rare or out-of-vocabulary words. Each unique token in the vocabulary is then assigned a numerical ID, and the input text is converted into a sequence of these IDs. This numerical representation is what the model uses for training and inference. Different tokenization schemes exist, such as Byte Pair Encoding (BPE), WordPiece, and SentencePiece, each with its own trade-offs in terms of vocabulary size, handling of rare words, and computational efficiency. Understanding tokenization is crucial because it directly affects the model's performance, especially in tasks involving text generation, translation, and understanding.