1Z0-1127-25 LLM Fundamentals Practice Question
A team is evaluating two embedding models for a similarity search task. Model A has a higher BERTScore on a reference dataset. Model B has a lower perplexity on the same dataset. Which model is likely better for retrieval?
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
✓
Model A, because BERTScore directly measures semantic similarity of embeddings
For retrieval tasks, embedding quality is best measured by semantic similarity metrics like BERTScore, which correlate with how well embeddings capture meaning. Perplexity measures language model fluency, not embedding quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Both are equally good for retrieval
Why it's wrong here
The two metrics measure different aspects; BERTScore is more relevant for embedding quality in retrieval.
- ✓
Model A, because BERTScore directly measures semantic similarity of embeddings
Why this is correct
BERTScore is a semantic similarity metric that evaluates the quality of embeddings for capturing meaning, which is crucial for retrieval.
- ✗
Model B, because lower perplexity indicates better language modeling, which improves retrieval
Why it's wrong here
Perplexity is a measure of how well the model predicts tokens; it does not directly assess the quality of embeddings for similarity search.
- ✗
Neither metric is relevant for retrieval tasks
Why it's wrong here
BERTScore is relevant for evaluating semantic similarity of embeddings, which is important for retrieval.
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