Overconfidence Problem

The overconfidence problem in machine learning refers to models exhibiting unrealistically high confidence in their predictions, even when incorrect. Current research focuses on mitigating this issue across various model types, including large language models (LLMs), neural networks for image and tabular data, and recommendation systems, employing techniques like knowledge transfer, cautious calibration, and counterfactual explanations to improve prediction accuracy and calibration. Addressing overconfidence is crucial for building trustworthy AI systems, enhancing human-AI collaboration, and ensuring reliable deployment in high-stakes applications where miscalibration can have significant consequences.