seniorDeep Learning
What is Attention Masking in Transformers and why is it essential for sequence modeling?
Updated May 16, 2026
Short answer
Attention masking controls which tokens a model can attend to, ensuring proper handling of padding tokens and autoregressive constraints.
Deep explanation
Attention masking is a crucial mechanism in Transformer models that modifies the attention matrix to enforce structural constraints on token interactions.
Why it is needed:
- Sequences often contain padding tokens.
- In language modeling, future tokens must not be visible.
Types of masks:
- Padding Mask:
- Prevents attention to padding tokens.
- Ensures computation ignores meaningless input positions.
- Causal Mask (Look-ahead mask):
- Used in decoder-only models (GPT).
- Prevents token from attending to future tokens.…
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