Masked Language Modeling
Definition
Masked language modeling trains a model to predict hidden tokens from their context.
A training objective where hidden tokens are reconstructed from their context.
Simply explained
Like a cloze test, where missing parts are filled in using the words before and after.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
In a sentence, a word is masked and the model learns to predict it from both sides of the context.
Limits and pitfalls
- Equating MLM with autoregressive text generation or not documenting the masking strategy.
Origin & history
BERT made MLM a central pretraining method for bidirectional transformer encoders in 2018.
Use in mid-sized companies
- Possible application: A development team tests a masked language model for a clearly defined text task. Masking strategy, test data, and quality are documented; the approach is separated from free text generation.
Frequently asked questions
What does Masked Language Modeling mean?
A training objective where hidden tokens are reconstructed from their context.
Which mistakes should be avoided?
Equating MLM with autoregressive text generation or not documenting the masking strategy.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: