Tokenization

Tokenization involves breaking down text into smaller units, or tokens, such as words, subwords, or characters. For example, “artificial” might be split into “art,” “ific,” and “ial.” This process is vital because LLMs process numerical representations of tokens, not raw text. Tokenization enables models to handle diverse languages, manage rare or unknown words, and optimize vocabulary size, enhancing computational efficiency and model performance.

LLMs use subword tokenization, like Byte-Pair Encoding (BPE), to break OOV words into known subword units. For instance, “cryptocurrency” might split into “crypto” and “currency.” This approach allows LLMs to process rare or new words, ensuring robust language understanding and generation.