This started feeling manageable and then kept expanding.
Start by defining tokenization and its role in LLMs, then systematically cover tokenization schemes (BPE, WordPiece, Unigram), and analyze trade-offs in vocabulary size, memory/compute costs, attention complexity, and multilingual/code-specific considerations. Use concrete examples and quantify impacts where possible to demonstrate depth.
Pro tip: Emphasize that tokenization is a critical but often overlooked design choice that directly impacts model efficiency and fairness across languages; mention that at Scale.ai, handling diverse data requires adaptive tokenization strategies.
Explain that tokenization converts raw text into tokens (subword units) that the model processes, bridging raw data and embeddings. Highlight that it's the first step in the LLM pipeline and affects everything downstream.
Compare BPE, WordPiece, and Unigram/SentencePiece, noting their algorithms (frequency-based merging vs. probabilistic) and typical use cases (e.g., BPE for GPT, WordPiece for BERT).
Discuss how larger vocabularies reduce sequence length (lower compute/attention cost) but increase embedding matrix size and softmax cost; smaller vocabularies increase sequence length but reduce memory. Mention the balance between OOV rates and model size.
Explain that attention is O(n^2) in sequence length, so tokenization affects n; larger vocab reduces n but increases embedding parameters. Also note memory for embeddings and compute for softmax over vocabulary.
Discuss challenges like script diversity, code syntax, and rare tokens; solutions like byte-level BPE, language-specific tokenizers, or larger vocabularies to cover multiple languages/code. Mention trade-offs in fairness and performance.
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