NCA-GENL Core Machine Learning and AI Knowledge Practice Question
A team is deploying a large language model for real-time text generation. They observe that the model sometimes produces repetitive and dull outputs, especially when generating longer sequences. They want to encourage more diverse and creative text without significantly degrading coherence. Which decoding strategy should they consider?
⚠ Common exam trap
The trap here is thinking that beam search or low temperature improves creativity, when they actually make outputs more deterministic and repetitive.
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-k sampling
Top-k sampling introduces controlled randomness by sampling from the top k tokens, which increases diversity and reduces repetition. It avoids the pitfalls of greedy and beam search, which are deterministic and prone to dull outputs, while preventing the incoherence that can come from sampling the entire distribution.
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 with a low temperature
Why it's wrong here
A low temperature makes the distribution sharper, reducing randomness and making the model more deterministic. This would increase repetitiveness, not reduce it. In this scenario, lowering temperature would worsen the dull outputs, so it is not the right choice.
- ✗
Beam search
Why it's wrong here
Beam search explores multiple hypotheses but still tends to produce safe, high-probability sequences, which can be repetitive. It is designed for accuracy rather than diversity. In real-time generation, beam search may also be computationally expensive and does not directly address the dullness issue.
- ✗
Greedy search
Why it's wrong here
Greedy search selects the highest-probability token at each step, which often leads to repetitive and generic outputs. It does not promote diversity. In this scenario, greedy search would exacerbate the repetitiveness problem because it always chooses the most likely continuation, lacking randomness.
- ✓
Top-k sampling
Why this is correct
Top-k sampling restricts the next token choices to the k most likely tokens and samples from them, introducing randomness while avoiding very low-probability tokens. This promotes diversity and reduces repetition. In this scenario, it balances creativity and coherence better than deterministic methods, making it suitable for real-time generation.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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