1Z0-1127-25 LLM Fundamentals Practice Question
What is the key advantage of multi-head attention over single-head attention in transformer models?
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
✓
It allows the model to focus on different parts of the sequence simultaneously from different representation subspaces
Multi-head attention allows the model to attend to information from different representation subspaces at different positions, capturing a richer understanding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It eliminates the need for positional encoding
Why it's wrong here
Positional encoding is still needed to capture order information.
- ✗
It reduces the total number of parameters
Why it's wrong here
Multi-head attention increases parameters (or keeps similar count with smaller dimensions).
- ✓
It allows the model to focus on different parts of the sequence simultaneously from different representation subspaces
Why this is correct
Each head learns different attention patterns, improving model capacity.
- ✗
It makes the model non-autoregressive
Why it's wrong here
Multi-head attention does not affect autoregressive property.
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