What is attention collapse in large transformer models?

Attention collapse occurs when attention distributions become overly concentrated or uniform, reducing learning effectiveness.

During training, attention heads may converge to degenerate patterns where they either focus on a single token or distribute weights uniformly. This reduces representational diversity and model performance. Techniques like entropy regularization and head pruning mitigate this.