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Mechanistic Investigation of In-Context Learning
In-context learning (ICL) is when a model learns at inference-time to do a task from examples given in its prompt. You show it a few input-output pairs, and given a new input, it produces the right output. The model learns what to do on-the-fly all within the forward pass.