An early-exit rule with a zero-shot fallback, calibrated by Learn-then-Test risk control, keeps the average loss from corrupted in-context demonstrations under a preset bound.
L ink P rompt: Natural and universal adversarial attacks on prompt-based language models
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Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting
An early-exit rule with a zero-shot fallback, calibrated by Learn-then-Test risk control, keeps the average loss from corrupted in-context demonstrations under a preset bound.