1Z0-1127-25 Fundamentals of Large Language Models Practice Question
Which THREE techniques are commonly used to improve the quality of text generation?
⚠ Common exam trap
Oracle often tests the misconception that greedy decoding or random sampling are valid quality-improvement techniques, when in fact they either cause repetition (greedy) or incoherence (random) without the controlled stochasticity of temperature, top-k, or the global optimization of beam search.
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 scaling
Temperature scaling is correct because it controls the randomness of token probability distributions by dividing logits before softmax; lower temperatures (e.g., 0.1) make the model more deterministic, while higher temperatures (e.g., 1.5) increase diversity. This directly influences the quality of generated text by balancing coherence and creativity.
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 scaling
Why this is correct
Temperature scaling smooths token probabilities and can improve the quality-diversity trade-off.
- ✓
Top-k sampling
Why this is correct
Top-k sampling limits choices to the k most likely tokens, improving quality.
- ✗
Greedy decoding
Why it's wrong here
Greedy decoding is fast but often produces less diverse and lower quality outputs compared to beam search.
- ✗
Random sampling
Why it's wrong here
Pure random sampling often reduces quality due to high unpredictability.
- ✓
Beam search
Why this is correct
Beam search explores multiple possible sequences to find the best output.
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