Seonglae ChoMassive Activation
Special token, delimiter, conjunction, preposition, first token, number token, weak semantics
This massive activation functions like a constant bias within the model, where setting it to 0 degrades performance while maintaining performance when fixing to mean value. During LayerNorm/RMSNorm, large values dominate the variance, causing the token's normalization vector to become distinctive and propagate through Q/K/V, inducing attention. Adding explicit attention bias parameters (k′, v′) during training eliminates the massive activation phenomenon while preserving model performance.
arxiv.org
https://arxiv.org/pdf/2402.17762
When it emerges (ICLR 2025 spotlight)
During the Softmax normalization process, internal dependencies are created between tokens, causing them to act like a bias on the Key side. They contribute very little to the actual meaningful values.
arxiv.org
https://arxiv.org/pdf/2410.10781