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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Jul 27·5 min read
Interior interpretability with attention rollout: contraction and propagation profiles in Transformers
TL;DR
Interior interpretability is introduced as a method to analyze how attention mechanisms in Transformers propagate information through layers. Using attention rollout, the study reveals that deeper layers exhibit stronger contraction profiles, indicating a structured propagation of feature importance.
✦ Why It Matters
Use attention rollout to diagnose and understand feature propagation in your Transformer models today.
Key Takeaways
- Implement attention rollout to analyze feature propagation in Transformers.
- Explore contraction profiles to understand model behavior at different depths.
- Compare attention rollout results with other attribution methods for insights.
- Use findings to refine model training strategies for better interpretability.
How It Works
Attention rollout is treated as a row-stochastic operator that captures how attention influences the flow of information between feature tokens in a Transformer. By applying contraction theory, the study quantifies how closely the rollout operator resembles a rank-one stochastic matrix, providing insights into the structure of information propagation across layers.