Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A data science team is fine-tuning a large language model using Vertex AI to generate marketing copy. They notice that the generated text is often repetitive and lacks creativity. Which technique should they apply to improve output diversity?
⚠ Common exam trap
Google Cloud often tests the misconception that decreasing sampling thresholds (like top-k or beam width) increases diversity, when in fact they reduce the candidate pool and make output more deterministic.
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 temperature parameter to 0.9.
Increasing the temperature parameter to 0.9 raises the randomness of the probability distribution over tokens, allowing less likely tokens to be selected. This directly counteracts repetitive output by encouraging the model to explore more diverse word choices, which is a standard technique for improving creativity in text generation.
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 temperature parameter to 0.9.
Why this is correct
Temperature controls the randomness of token sampling during generation. Raising it to 0.9 flattens the probability distribution, so lower-probability tokens are selected more often, directly countering the repetitive, low-creativity output observed in the marketing copy.
- ✗
Decrease the beam search width to 1.
Why it's wrong here
Beam search is deterministic and returns the single highest-probability sequence, so narrowing the width to 1 removes exploration entirely and increases repetition. Beam search suits translation or summarisation where accuracy matters; sampling methods such as temperature and top-p produce the diversity wanted here.
- ✗
Decrease the top-k sampling threshold.
Why it's wrong here
Lowering top-k restricts sampling to fewer highest-probability tokens, which sharpens and further repeats output rather than diversifying it. Raising top-k, or tuning temperature and top-p, is the lever for creativity; low top-k suits tasks demanding deterministic, factual responses.
- ✗
Add more examples of repetitive text to the training dataset.
Why it's wrong here
Adding repetitive examples reinforces the exact pattern the team wants removed, biasing the fine-tuned weights toward duplicated phrasing. Additional training data helps when the model lacks domain vocabulary or style, not when decoding settings are causing the repetition.
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