Entropy-aware Masking for Masked Language Modeling
Masked language modeling has become a standard pretraining objective for training encoder-based language models. In this approach, certain tokens in the input are masked, and the model learns to predict them using the surrounding context. This process enables the model to capture both syntactic and semantic properties of language. Conventionally, the tokens selected for masking are chosen at random, which may not always yield the most effective learning signals. In this work, we examine a token masking strategy based on entropy distribution. We use the model's entropy over token predictions to identify which tokens should be masked. This method aims to target tokens that are more informat...
| Authors | Gokul Srinivasagan, Kai Hartung, Munir Georges |
|---|---|
| Primary category | cs.AI |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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