Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A model generates responses that frequently repeat phrases or words. Which parameter adjustment is most likely to fix this?
⚠ Common exam trap
It's easy for candidates to confuse repetition penalty with diversity-promoting parameters like temperature or top_k, mistakenly believing that increasing randomness or narrowing token selection will fix repetition, when in fact those adjustments can worsen the problem.
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
✓
Increase repetition penalty
Increasing the repetition penalty directly discourages the model from selecting tokens that have already appeared in the generated sequence, thereby reducing repetitive phrases or words. This parameter works by subtracting a fixed penalty from the logits of previously generated tokens before applying the softmax function, making them less likely to be chosen again.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase top_k
Why it's wrong here
Higher top_k increases the pool of tokens, which may encourage diversity but is less effective than repetition penalty.
- ✗
Increase temperature
Why it's wrong here
Higher temperature increases randomness, not necessarily reducing repetition.
- ✓
Increase repetition penalty
Why this is correct
Correct: Repetition penalty specifically reduces the likelihood of repeating tokens.
- ✗
Increase max output tokens
Why it's wrong here
Longer output may actually allow more repetition.
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