What is Byte Pair Encoding in LLM tokenizers?
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Simple meaning
Byte Pair Encoding starts from characters or bytes and repeatedly merges the most frequent adjacent pairs into new vocabulary items.
WHY — Tokens instead of words?
LLMs use tokens (not full words) because it helps them:
who only read docs from people who shipped.
and tied to GenAI / LLM work.
Each piece maps to a number the network can learn.
Fixed pieces are what transformers expect as input.
STEPS — What happens with tokens?
Before the model can read a sentence, it goes through these steps:
- 1Byte Pair Encoding starts
from characters or bytes and repeatedly merges the most frequent adjacent pairs into new vocabulary items.
- 2The result is a
subword vocabulary that handles rare words without a huge word list.
- 3GPT-style models commonly use
BPE or a close variant.
- 4Context mix
Attention looks at nearby tokens together.
- 5Next token
The model scores what should come next.
- 6Decode
IDs turn back into readable text.
EXAMPLE — See it in action
Let's see how a real sentence is tokenized (tokens may vary by model):
Note: Actual tokens and IDs depend on the tokenizer (e.g., GPT, Llama, etc.).
Key takeaway
Byte Pair Encoding starts from characters or bytes and repeatedly merges the most frequent adjacent pairs into new vocabulary items. The result is a subword vocabulary that handles rare words without a huge word list.