Junior interview questionAsked at Netflix · interview write-up
What is subword tokenization, what does SentencePiece add, and what are the main transformer variants?
Model answer
Subword tokenizers keep common words whole and split rare ones into frequent pieces: no unknown words, short sequences. SentencePiece trains on raw text, spaces included, so it needs no language-specific pre-splitting.
Variants: encoder-only like BERT for understanding, decoder-only like GPT for generation, and encoder-decoder like T5 for input-to-output tasks such as translation.
Practise the rest
11 Tokenization questions, 492 in all, each with a model answer. Free.
More from Netflix interview questions.
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?
- How does byte pair encoding learn a vocabulary, and how does it then encode a word it never saw in training?
- 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?