hardMultiple Select
Generative AI Leader Practice Question: A data scientist is evaluating the output quality…
A data scientist is evaluating the output quality of a text generation model. They observe that the model often repeats phrases and produces very generic responses. Which THREE parameter adjustments could help increase diversity and reduce repetition? (Choose three.)
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 the top-k value from 40 to 100
Increasing the top-k value from 40 to 100 (option A) widens the candidate token pool at each generation step, so the model is no longer restricted to only the 40 most probable tokens and can select from a broader, more varied set, which reduces repetitive and generic phrasing. Increasing the temperature from 0.5 to 0.9 (option B) flattens the softmax probability distribution, giving lower-probability tokens a greater chance of being sampled and thereby producing more diverse, less deterministic text. Increasing the top-p value from 0.8 to 0.95 (option D) expands nucleus sampling to include tokens cumulatively covering 95% of the probability mass instead of 80%, again enlarging the sampling pool and lowering repetition. Option C (decreasing max output tokens from 1024 to 512) only shortens the response length and does not change the sampling distribution, so it does not increase diversity. Option E (decreasing the frequency penalty to 0.0) removes or weakens the penalty that discourages repeated tokens, which would tend to increase repetition rather than reduce it.
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 the top-k value from 40 to 100
Why this is correct
Raising top-k from 40 to 100 widens the candidate pool sampled at each step, so more lower-probability tokens can be chosen. This increases lexical variety and reduces the repetitive, generic phrasing the data scientist observed.
- ✓
Increase the temperature from 0.5 to 0.9
Why this is correct
Raising temperature from 0.5 to 0.9 flattens the softmax probability distribution over the vocabulary, so lower-probability tokens are sampled more often. This directly counteracts the repetitive, generic phrasing described in the stem by increasing output diversity.
- ✗
Decrease the max output tokens from 1024 to 512
Why it's wrong here
Max output tokens caps response length, not token selection, so shortening it truncates output while leaving the repetition probability distribution untouched. It is tempting because shorter answers feel less repetitive, but this parameter is intended for controlling cost and latency, not for diversifying sampling.
- ✓
Increase the top-p value from 0.8 to 0.95
Why this is correct
Raising top-p from 0.8 to 0.95 widens the nucleus of tokens considered at each step, so lower-probability words become selectable. This directly counteracts the repetitive, generic output by reducing the model's tendency to lock onto the same high-probability phrases.
- ✗
Decrease the frequency penalty to 0.0
Why it's wrong here
A frequency penalty of 0.0 applies no penalty at all, so repeated tokens remain as likely as before and repetition persists. It is tempting because zeroing a penalty sounds like removing a constraint on the model, but the frequency penalty is precisely the mechanism that discourages verbatim reuse of tokens already emitted.
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