MIT Calibrates AI Confidence to Cut Down on Hallucinations
MIT CSAIL researchers propose RLCR (Reinforcement Learning with Calibration Rewards), using the Brier Score to penalize over-confident wrong answers and reward well-calibrated uncertainty, leading to more reliable LLM outputs and the ability to admit when they don’t know—potentially increasing safety for high-stakes applications.