Tag
A new research paper introduces RLMF (Reinforcement Learning with Metacognitive Feedback), a two-stage approach that uses the model's own self-judgments to calibrate confidence and express uncertainty faithfully, achieving state-of-the-art calibration across diverse tasks while preserving accuracy and surpassing standard RL by up to 63%.
This paper introduces reinforcement learning with metacognitive feedback (RLMF) and metacognitive data selection to improve large language model calibration, enabling faithful expression of intrinsic uncertainty and surpassing standard RL by up to 63%.