Typhoon: Gradient-Based Task-Specific Masking for Masked Language Models
The choice of which tokens to mask is an under-examined design decision in masked language modeling (MLM). Standard pretraining masks tokens uniformly at random, whereas content-aware masking schemes can improve downstream transfer. We study masking as a task-adaptive component of the fine-tuning pipeline and introduce Typhoon, a masking strategy that uses the gradient of the MLM loss with respect to one-hot token inputs to estimate online how much each token type contributes to the objective. Typhoon maintains an exponential moving average of per-token-type saliency and calibrates these scores into a masking distribution whose expected masking rate matches a target budget under a token-independence approximation. We formalize the method and evaluate it against random masking and whole-word masking on two GLUE tasks, MRPC and CoLA, across three BERT-family backbones (TinyBERT, DistilBERT, and BERT-base) and five random seeds per configuration ($90$ training runs in total). Once seed variance is accounted for, no masking strategy is reliably better than the others on these tasks: on MRPC the gap between Typhoon and the best baseline stays within $0.004$ $F_1$, across all twelve Typhoon comparisons no paired test reaches significance, and every $95\%$ confidence interval contains zero. Inspecting Typhoon's learned distribution explains why: budget-preserving min-max calibration compresses the gradient ranking into a narrow band whose Kullback-Leibler divergence from uniform random masking is $7\times 10^{-4}$ nats per token, and widening the calibration spread by an order of magnitude in KL still leaves downstream accuracy unchanged within seed noise. Typhoon's apparent advantage in single-run experiments does not survive multi-seed evaluation, showing that gradient-based task-adaptive masking is competitive with, but does not outperform, uniform random masking at this scale.