Beginner LevelAI, LLMs, Embeddings & MCP
What is Tokenization in LLMs and how does Byte-Pair Encoding (BPE) work?
❓ Interview Prompt: Explain tokenization, vocabulary dictionaries, Byte-Pair Encoding (BPE), and token-to-word ratios.
Tokenization is the process of converting raw text strings into a sequence of numeric integers (tokens) that a Large Language Model can process.
How Byte-Pair Encoding (BPE) Works:
1
Starts with a base vocabulary of individual characters or bytes.
2
Iteratively counts pairs of adjacent characters/tokens in a large training text corpus.
3
Merges the most frequently occurring token pairs into new single sub-word tokens.
4
Repeats until reaching a target vocabulary size (e.g. 32,000 to 100,000 tokens).
Key Rules:
•
Common words (e.g.
"learning") become single tokens.•
Rare words or non-English characters are split into multiple sub-word tokens.
•
Rule of thumb: 100 tokens ≈ 75 English words.
Code Implementation Example
typescript
import { get_encoding } from 'tiktoken';
const encoder = get_encoding('cl100k_base'); // Tokenizer used by GPT-4
const text = "Tokenization breaks text into sub-words!";
const tokens = encoder.encode(text);
console.log('Token IDs:', tokens); // [30667, 1634, 15822, ...]
console.log('Token Count:', tokens.length);
encoder.free(); // Free WASM memory💡Measuring token counts locally using tiktoken BPE encoder.
🎯 Interview Key Takeaway
LLMs process numeric tokens generated by sub-word tokenizers like BPE, not raw text characters.
#Tokenization#BPE#Tiktoken#Beginner
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