Generative AI Leader Fundamentals of Generative AI Practice Question
A team is tuning a large language model for a question-answering task. They notice the model gives high confidence scores to answers that are factually incorrect. Which evaluation metric should they primarily use to detect this overconfidence problem?
⚠ Common exam trap
Google Cloud often tests the distinction between intrinsic evaluation metrics (like perplexity) and calibration metrics, leading candidates to mistakenly choose perplexity when the core issue is confidence miscalibration rather than general model uncertainty.
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
✓
Expected Calibration Error (ECE)
Expected Calibration Error (ECE) directly measures the alignment between a model's predicted confidence and its actual accuracy. In this scenario, high confidence on incorrect answers indicates miscalibration, and ECE quantifies this mismatch by binning predictions by confidence and computing the average absolute difference between accuracy and confidence per bin.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perplexity
Why it's wrong here
Perplexity indicates model surprise, not confidence in correctness.
- ✓
Expected Calibration Error (ECE)
Why this is correct
ECE directly quantifies how well confidence scores reflect actual correctness.
- ✗
BLEU score
Why it's wrong here
BLEU evaluates text similarity, not confidence calibration.
- ✗
ROUGE-L
Why it's wrong here
ROUGE-L measures recall of longest common subsequence, not calibration.
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