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This paper investigates why instruction-tuned LLMs are overconfident in their own responses, identifying an 'ownership bias' that gives higher confidence to self-generated answers. It proposes a simple inference-time strategy to reframe the model's answer as user input, improving calibration by up to 26% without retraining.