Teacher Forcing
Teacher forcing trains a sequential decoder on the ground-truth previous token instead of its own prediction — fast and biased, which is why exposure bias exists.
Definition
Teacher Forcing refers to truth previous token instead of its own prediction — fast and biased, which is why exposure bias exists. Keep that definition fixed when comparing series, managers, or regimes — renaming the same tape does not create a new signal.
Why it matters
It binds model output to retrieval, tools, or evaluation so answers stay grounded instead of free-floating. When truth previous token instead of its own prediction — fast and biased, which is why exposure bias exists shifts, related hedges, limits, and narratives usually need an explicit update rather than a quiet assumption.
Case
Suppose a desk is positioned for the opposite of what teacher forcing is saying. If truth previous token instead of its own prediction — fast and biased, which is why exposure bias exists moves against that book, the first question is not “is the story clever?” but whether size, hedges, and stop logic still match the observation.
How to read it
Measure grounding rate, latency, and failure modes under missing context — not demo chat quality alone. Prefer a short written null hypothesis for Teacher Forcing: what would falsify the current reading in the next window?