Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which THREE approaches are effective for reducing bias in generative model outputs? (Choose three.)
⚠ Common exam trap
A common mix-up: candidates confuse randomness (high temperature) with fairness, or mistake adversarial training (a robustness technique) for a bias mitigation method, when in fact bias reduction requires data-level or fine-tuning interventions like balanced datasets, debiased fine-tuning, or prompt engineering.
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
✓
Use a balanced training dataset.
A balanced training dataset reduces the risk of the model learning spurious correlations or skewed distributions that lead to biased outputs. By ensuring that all demographic groups, topics, or perspectives are represented proportionally, the model's learned probability distribution is less likely to favor one group over another, directly mitigating representation bias at the data level.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set temperature to a very high value.
Why it's wrong here
Temperature affects randomness, not bias.
- ✗
Use adversarial training.
Why it's wrong here
Adversarial training is not a standard bias reduction method.
- ✓
Use a balanced training dataset.
Why this is correct
Balanced data reduces representation bias.
- ✓
Use prompt engineering to specify neutral tone.
Why this is correct
Prompts can guide model away from biased language.
- ✓
Fine-tune on a debiased dataset.
Why this is correct
Fine-tuning with debiased data reduces learned biases.
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