1Z0-1127-25 LLM Fundamentals Practice Question
A developer is using OCI Generative AI with a Cohere Command model for text generation. They want the output to be more creative and diverse, but still relevant. Which sampling strategy should they use?
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
✓
Top-p (nucleus) sampling
Top-p (nucleus) sampling selects from the smallest set of tokens whose cumulative probability exceeds p. It adapts to the model's confidence, allowing diversity while maintaining relevance. Greedy decoding is deterministic, temperature scales all probabilities, top-k fixes the number of candidates, and beam search explores multiple sequences but tends to produce safe outputs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Temperature sampling (temperature > 1)
Why it's wrong here
Temperature > 1 flattens the probability distribution, increasing randomness but may cause irrelevant tokens to be selected.
- ✗
Top-k sampling
Why it's wrong here
Top-k sampling selects from the k most likely tokens. It can be too restrictive when the distribution is flat, or too loose when peaked.
- ✓
Top-p (nucleus) sampling
Why this is correct
Top-p sampling dynamically chooses the set of tokens with cumulative probability p, balancing creativity and relevance by adapting to the model's confidence.
- ✗
Greedy decoding
Why it's wrong here
Greedy decoding always picks the token with the highest probability, leading to deterministic, less creative outputs.
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