57 - Attention Visualization | Mango Encyclopedia

Introduction

Attention visualization refers to techniques for visualizing and interpreting attention weights in neural networks. Attention matrices can be visualized as heatmaps showing which tokens attend to which other tokens, helping us understand what the model has learned.

Why Visualize Attention?

Interpretability: Understand what information the model focuses on
Debugging: Identify if model is looking at correct parts
Insights: Discover patterns learned by the model
Trust: Verify model is reasoning correctly

Visualization Methods

1. Attention Heatmap

Matrix visualization where color intensity shows attention weight:

Rows: query positions
Columns: key positions
Color: attention weight (darker = higher)

2. Edge Drawing

Draw lines from attending token to attended token, with thickness indicating weight.

3. Token Highlighting

Highlight source tokens by how much they're attended to.

Attention Patterns to Look For

Pattern	What it indicates
Diagonal	Local structure (adjacent tokens attend)
Vertical [CLS]	CLS token aggregating information
Strong connections	Syntactic/semantic relationships
Uniform	Model may not be learning meaningful patterns

Tools for Visualization

Transformers library: attention visualization utilities
exBERT: Interactive visualization tool
TensorBoard: Attention logging
Custom plots: matplotlib/seaborn heatmaps

Test Your Understanding

Question 1: Attention visualization shows:

A) Model architecture
B) Which tokens attend to which other tokens
C) Training loss
D) Weight values only

Question 2: A diagonal pattern in attention heatmap indicates:

A) No attention
B) Local structure (adjacent tokens attend to each other)
C) Random pattern
D> Long-range only

Question 3: Strong vertical line at [CLS] position indicates:

A) CLS token aggregates information from all tokens
B) No aggregation
C) Model is failing
D) Random attention

Question 4: Attention visualization helps with:

A) Only speed
B) Interpretability and debugging
C) Training only
D) No benefit

Question 5: Uniform (flat) attention across all positions might indicate:

A) Model is learning meaningful patterns
B) Model may not be learning meaningful patterns
C> Perfect model
D> High quality

Question 6: Attention heatmap rows and columns represent:

A) Layer numbers
B> Rows: query positions, Columns: key positions
C) Batch size
D) Head numbers

Question 7: exBERT is a tool for:

A) Training
B) Interactive attention visualization
C) Model compression
D) Data processing

Question 8: Edge drawing visualization shows:

A> Lines from attending token to attended token
B> Only text
C) No connections
D) Random

57. Attention Visualization