Gabrielle Kaili-May Liu
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The paper introduces a novel framework to quantify faithful confidence expression (FC) in Large Reasoning Models (LRMs), finding that FC remains a significant and challenging reliability target for these models.
This paper introduces two novel mechanisms, reinforcement learning with metacognitive feedback (RLMF) and metacognitive data selection, to enhance language model metacognition and achieve faithful calibration.
Papers
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
This paper introduces two novel mechanisms, reinforcement learning with metacognitive feedback (RLMF) and metacognitive data selection, to enhance language model metacognition and achieve faithful cal…