In-context Learning and Induction Heads

How tiny pattern-finders inside AIs help them learn from a prompt

Researchers found small parts inside big language programs that act like pattern detectors, finishing sequences such as A B .
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A -> B.
These parts, called induction heads, seem to appear right when the model suddenly gets better at learning from the prompt itself — a kind of short-term learning called in-context learning.
Training shows a clear sudden jump in ability at the same moment those pattern-finders develop, visible as a little bump in the loss trace.
The team shows multiple, complementary lines of evidence that point to those parts being the main mechanism, at least in many cases.
For tiny attention-only models they even have strong causal proof; for bigger models with extra layers the link is more correlational, not fully proved yet.
This simple idea — small components that spot repeated patterns and then predict the next item — could explain a lot about how models learns from examples on the fly, and might help us build smarter, safer tools going forward.

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In-context Learning and Induction Heads

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