Junior interview question
How does byte pair encoding learn a vocabulary, and how does it then encode a word it never saw in training?
Model answer
Training repeatedly merges the most frequent adjacent pair of symbols into a new one, giving an ordered merge list. Encoding splits new text into base symbols and replays the merges in order, so an unseen word becomes several known pieces. GPT-2's 50,257 entries are 256 byte tokens, 50,000 merges and one end-of-text token.
Practise the rest
11 Tokenization questions, 492 in all, each with a model answer. Free.
More Tokenization interview questions
- When you send a prompt to a language model, what does the model actually receive?
- What is a token, and why do most models use subword tokens rather than whole words or single characters?
- A colleague estimates next month's API bill by counting the words in a batch of English and Hindi support tickets and multiplying by the price per token. What's wrong with the estimate, and how would you do it instead?
- You're moving a product from a model that uses cl100k_base to one that uses o200k_base. Nothing crashes in staging. What could still be broken, and what goes into the migration plan?
- A ticket summariser crashes because tiktoken raises ValueError on a ticket that contains the literal end-of-text marker. A teammate's fix is to pass allowed_special='all'. What's your review comment?
- Compare the main NLP tokenization approaches. Then, how would you design a recommender that uses an LLM?