AI / LLM Basics Interview Questions
What is the Vocabulary of an LLM?
An LLM's vocabulary is the complete, fixed set of tokens it was trained to recognize and generate, typically containing tens of thousands of entries.
- Built once during tokenizer training and then kept fixed for that model's entire lifetime
- Includes whole words, sub-word pieces, punctuation, and often special-purpose tokens for things like marking the start or end of a message
- Any input text gets broken down into pieces that exist somewhere in this vocabulary, no matter how unusual the input is
The vocabulary size is a real design trade-off: a larger vocabulary means more common words fit in a single token, but it also means a bigger, more expensive output layer for the model to compute over.
What is an LLM's vocabulary?
What trade-off comes with a larger vocabulary?
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