Tokenization: The Building Blocks of Natural Language Processing Hosted by Nathan Rigoni (no guest)
In this first half of the NLP mini‑series, Nathan breaks down how computers turn raw text into numbers that machines can manipulate. He explains the evolution from naïve “split‑by‑space” word indexing to modern sub‑word tokenization, shows why tokens are both the engine and the bottleneck of today’s large language models, and highlights the numeric and linguistic challenges that still limit AI performance. How can we redesign tokenization so models can understand numbers and rare words without exploding in size?
What you will learn
- The basic “word‑to‑integer” tokenization method and why it fails at web‑scale vocabularies.
- Sub‑word tokenization (syllable‑like prefixes, suffixes, and character‑level tokens) and its typical vocabulary size (~100 k).
- How token limits (≈ 2 million tokens for state‑of‑the‑art models) affect context length and memory usage.
- The impact of token granularity on numeric handling (e.g., different token splits of “100 000”) and on counting characters (the classic “R in strawberry” problem).
- Why the bits‑per‑parameter metric (≈ 3–4 bits) is tied to preserving every token across the model’s forward pass.
Resources mentioned
- Original word‑level tokenization (space‑split) – basic concept.
- Sub‑word tokenization methods such as Byte‑Pair Encoding (BPE) and WordPiece (used by GPT, BERT, etc.).
- Papers on bits‑per‑parameter efficiency in large language models (to be covered in future “paper review” episodes).
- Example numeric tokenization challenges (e.g., different token splits for 100 000).
Why this episode matters
Tokenization is the foundation of every downstream NLP task—from document classification to chatbots. Understanding its limits explains why models hallucinate, struggle with math, or miscount characters, and points to research directions (better token schemes, dynamic chunking, or byte‑level models) that could unlock longer contexts and more accurate reasoning. For anyone building or fine‑tuning language models, mastering tokenization is the first step toward more reliable AI.
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Keywords: tokenization, sub‑word tokenization, BPE, WordPiece, NLP basics, large language model limits, token length, numeric tokenization, bits‑per‑parameter, contextual AI.
