A bidirectional language model estimates or represents text using both preceding and following context around a target token. This approach supports stronger contextual understanding than models that rely only on earlier text, especially for tasks involving masked words, classification, and semantic interpretation.
A language model that determines the probability that a given token is present at a given location in an excerpt of text based on the preceding and following text.