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Generative AI Leader Practice Question: Which parameter controls the randomness of a…
Which parameter controls the randomness of a language model's output?
⚠ Common exam trap
Google often tests the distinction between sampling parameters (temperature, top-k, top-p) and architectural parameters (embedding dimension, context window), so candidates may confuse top-k as a randomness control when it actually restricts the candidate pool rather than adjusting the probability distribution's entropy.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Temperature
Temperature is the parameter that directly controls the randomness of a language model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., >1.0) makes the probability distribution more uniform, increasing randomness, while a lower temperature (e.g., <1.0) sharpens the distribution, making the model more deterministic and focused on high-probability tokens.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Embedding dimension
Why it's wrong here
Embedding dimension is the size of the vector representation for tokens or documents, fixed at training time, and does not govern sampling randomness. It is tempting because it is a fundamental model parameter, and would be correct when selecting an embedding model for semantic search or retrieval quality.
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Context window
Why it's wrong here
The context window defines how many tokens the model can attend to, not the randomness of sampling. It is tempting because it is a core model parameter affecting output, and would be the correct choice when the requirement is handling longer inputs or retaining more conversation history within a prompt.
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Top-k
Why it's wrong here
Top-k truncates sampling to the k most probable tokens, limiting the candidate pool but not directly controlling randomness; temperature scales the probability distribution. It is tempting because it is a sampling parameter that shapes output variety, and would be correct when restricting generation to likely tokens to reduce nonsensical output.
- ✓
Temperature
Why this is correct
Temperature scales the sampling distribution's entropy: low values sharpen it toward greedy decoding, high values flatten it, increasing diversity. It is the sole parameter governing stochastic token selection, directly controlling output randomness as the stem asks.
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