Before revision: Multiple studies have shown that BERT is remarkably robust to pruning , yet few if any of its components retain high importance across downstream tasks . Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of scaling factors and biases in the output layer normalization (<0.0001\% of model weights).
After revision: Multiple studies have shown that Transformers are remarkably robust to pruning , yet few if any of its components retain high importance across downstream tasks . Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of scaling factors and biases in the output layer normalization (<0.0001\% of model weights).