Mid-level interview question
A teammate shows you a GPT-2 head that puts most of a pronoun's attention on the right noun and concludes that the head resolves pronouns. How do you respond?
Model answer
It's a lead to test, not a conclusion. Head 4.3 puts 0.659 on " ladder" from " it", but " did", " not" and " reach" also look mostly at " ladder", and the subject always sat in the first two tokens. That can't separate "finds the subject" from "likes an early position".
Attention weights show where a head reads from, not what the values carry or how 144 heads combine. I'd test sentences with the noun in different positions.
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More Attention interview questions
- Walk me through scaled dot-product attention. What are queries, keys and values, and why divide by the square root of d?
- What's inside one transformer block, and where do most of its parameters sit?
- What does the causal mask do, and what does it imply for a decoder model reading 'The ladder did not reach the window because it was too short'?
- Product wants to double the context you support, from 1,000 to 2,000 tokens. From the attention side, what gets more expensive, and what families of fixes would you weigh?
- Implement batched multi-head self-attention with a causal mask.
- A pre-norm transformer block trains badly. What do you check around the norms and residuals?