Quiz 30: Attention Heatmap

Test your understanding of attention heatmaps, multi-head attention visualization, and how attention scores evolve during model training.

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1. What does an attention heatmap visualize in NLP models?

Model accuracy over time

Token frequency

Relationships and focus between words based on attention scores

Word embeddings

2. What do the rows and columns represent in an attention heatmap?

Words attending and words being attended to

Input and output token embeddings

Document length and punctuation

Layer count and neuron ID

3. What does a brighter color in an attention heatmap cell typically indicate?

The presence of a special token

A longer input sentence

A higher attention score between two tokens

More padding in the sequence

4. Why do Transformer models use multi-head attention?

To reduce the embedding size

To speed up tokenization

To improve dropout rate

To capture different relationships and features across multiple perspectives

5. What is the role of attention heads in a heatmap visualization?

They determine the sentence length

They replace position embeddings

They mark punctuation importance

Each head focuses on a different aspect of word relationships

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