In-Context Learning
In-context learning is when a frozen language model improves at a task from examples placed in the prompt, without weight updates.
Definition
In-Context Learning refers to context learning is when a frozen language model improves at a task from examples placed in the prompt, without weight updates. 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 context learning is when a frozen language model improves at a task from examples placed in the prompt, without weight updates 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 in-context learning is saying. If context learning is when a frozen language model improves at a task from examples placed in the prompt, without weight updates 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 In-Context Learning: what would falsify the current reading in the next window?