Large Language Models (LLMs) require text to be converted into numerical representations called tokens before processing. A token can be a word, part of a word, or punctuation, and each is assigned a unique Token ID. Different LLMs use distinct tokenizers, leading to varied Token IDs for the same text. Algorithms like Byte-Pair Encoding (BPE), Byte-level BPE (BBPE), and WordPiece are employed to manage vocabulary size and sequence length, addressing issues like out-of-vocabulary words and long sequences inherent in character-level tokenization. The number of tokens processed directly impacts the model's context window and computational requirements. AI
IMPACT Understanding tokenization is crucial for optimizing LLM performance and managing context window limitations.
RANK_REASON Blog post explaining a core technical concept in LLMs.
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