AI / LLM Basics Interview Questions

What are Top-k and Top-p Sampling?

Top-k and top-p are two related techniques that limit which tokens a model is allowed to consider when picking its next word, rather than sampling from the full vocabulary every time.

Top-k sampling: restricts the choice to only the k highest-probability tokens
Top-p sampling, also called nucleus sampling: restricts the choice to the smallest set of tokens whose combined probability adds up to at least p

Both exist to avoid the model occasionally picking a bizarre, very-low-probability token purely by chance, while still allowing some genuine variety in its output, unlike always picking the single most likely token.

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What does top-k sampling restrict the choice to? Only the k highest-probability tokens

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Every single token in the vocabulary

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Only tokens starting with the letter K

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What is top-p sampling also known as? Nucleus sampling

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Deep sampling

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Binary sampling

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AI / LLM Basics Interview Questions

What are Top-k and Top-p Sampling?

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