In the context of AI and natural language processing (NLP), a token is a discrete unit of input text that a language model processes. Tokens typically correspond to words, subword segments (such as prefixes or suffixes), punctuation marks, or whitespace. The average token length in English is approximately 3 to 5 characters, though this varies depending on the tokenization algorithm employed (e.g., Byte Pair Encoding, WordPiece, or SentencePiece). For example:

• Dog → ["Dog"]
• Encyclopedia → ["Enc", "cyclo", "Pedia"]

Why are tokens important in AI or LLMs?

A token or process of tokenization is fundamental to language models, enabling them to interpret and generate text effectively. Tokens, created by a tokenizer, which is an algorithm or software tool that connects the human-readable text and model-ready input, determine how input data is segmented and processed, impacting model performance, efficiency, and accuracy. Proper tokenization helps manage complex language patterns, supporting a nuanced understanding within the context window.