This research presents a novel approach to improve the metacognitive abilities of large language models (LLMs) through reinforcement learning with metacognitive feedback (RLMF). The study identifies systemic deficiencies in LLMs, such as hallucination with high confidence and misrepresentation of uncertainty, which undermine their reliability. By implementing RLMF, the researchers demonstrate an improvement in the calibration of models' self-reported confidence scores, achieving state-of-the-art results in faithful calibration across diverse tasks. The findings indicate that RLMF not only surpasses standard reinforcement learning methods by up to 63% but also enhances LLMs' self-assessment capabilities, proposing a new paradigm for aligning model performance with intrinsic uncertainty.
Reinforcement Learning with Metacognitive Feedback Enhances Uncertainty Expression in Large Language Models
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