AINeutralarXiv – CS AI · 15h ago6/10
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When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning
Researchers reveal that correct demonstrations in in-context learning don't guarantee improved model performance—some accurate examples actually degrade accuracy. The study introduces task-preserving perturbations to show that exemplar utility depends on how demonstrations influence contextual inference, not merely on correctness, challenging conventional assumptions about how AI models learn from examples.