1Z0-1127-25 LLM Fundamentals Practice Question
A developer notices that a text generation model produces repetitive phrases when using greedy decoding. Which sampling strategy would best introduce controlled randomness to reduce repetition while maintaining coherence?
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
✓
Apply temperature sampling with temperature > 1
Temperature sampling scales the logits before applying softmax, controlling the randomness of token selection. A temperature > 1 increases diversity, reducing repetition, while still allowing the model to generate coherent text.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use greedy decoding but with a repetition penalty
Why it's wrong here
Repetition penalty is not a standard parameter of greedy decoding; it is typically used with sampling methods.
- ✓
Apply temperature sampling with temperature > 1
Why this is correct
Higher temperature flattens the probability distribution, making less likely tokens more probable, reducing repetition while maintaining coherence.
- ✗
Increase the beam width in beam search
Why it's wrong here
Beam search keeps multiple candidate sequences; while it can reduce repetition compared to greedy, it tends to produce more generic and still potentially repetitive outputs. It does not directly inject randomness.
- ✗
Disable top-k sampling
Why it's wrong here
Disabling top-k would revert to greedy or full sampling, not a targeted fix for repetition.
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