Mid-level interview questionAsked at Google · interview write-up
Compare the main NLP tokenization approaches. Then, how would you design a recommender that uses an LLM?
Model answer
Word vocabularies are huge and fail on unseen words; characters never fail but make sequences long. Subword methods, BPE, WordPiece and unigram, sit between: frequent words stay whole and rare ones split, so nothing is out of vocabulary.
For the recommender, keep a classic retrieval stage over item and user-history embeddings, and use the LLM to rerank and explain a short candidate list, since item IDs make poor tokens and catalogues change daily.
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11 Tokenization questions, 492 in all, each with a model answer. Free.
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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?