---
title: "元认知反馈强化学习激发大语言模型忠实不确定性表达"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2606.32032v1"
permalink: "https://feed.astralor.com/items/itm_bd9429f1e8e2dd0a195c7645"
published: "2026-06-30T17:56:01.000Z"
score: 59
channel: "ai"
tags: ["不确定性校准","元认知反馈强化学习","大语言模型","强化学习","忠实校准"]
---

# 元认知反馈强化学习激发大语言模型忠实不确定性表达

## 速览

本研究提出元认知反馈强化学习（RLMF）框架，通过奖励模型对自身性能判断的质量来优化生成排序，同时引入元认知数据选择以识别高价值训练样本。该框架应用于忠实校准任务，将内在不确定性映射为自然语言表达。实验表明RLMF在保持准确性的同时实现了通用且最先进的校准效果，相比标准强化学习提升了63%，增强了模型评估和表达自身能力边界的能力。

## 关键点

- 提出元认知反馈强化学习（RLMF），利用模型自我判断质量作为奖励信号
- 引入元认知数据选择，通过自我判断识别高价值训练样本
- 在忠实校准任务上实现最先进性能，超越标准强化学习63%
- RLMF保持任务准确性的同时增强模型不确定性表达能力

## 全文

Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in key metacognitive faculties: they hallucinate with high confidence, fail to recognize knowledge boundaries, and misrepresent their internal uncertainty--undermining trustworthiness and reliability. Since monitoring task performance and adapting behavior accordingly are central to metacognition, we posit that models capable of accurately judging their own performance are better positioned to improve it. We operationalize this idea via two novel mechanisms: reinforcement learning with metacognitive feedback (RLMF), a paradigm to refine completion rankings during preference optimization based on the quality of a model's self-judgments of performance, and metacognitive data selection, which uses similar self-judgments to identify high-value training examples, outperforming naive active learning. We apply these innovations to the problem of faithful calibration (FC), a task that is itself fundamentally metacognitive: the goal is to align expressed with intrinsic uncertainty, difficult even for frontier LLMs. We adopt a two-stage, decoupled approach, first using these methods to calibrate the faithfulness of models' self-reported confidence scores, then mapping to natural, context-adaptable linguistic uncertainty via targeted output editing. Extensive experiments show RLMF achieves generalizable, state-of-the-art FC on diverse tasks while preserving accuracy. Further, RLMF surpasses standard RL by up to 63% while enhancing models' ability to assess and express their own capability limits. This positions RLMF as a promising paradigm to enhance LLM metacognition toward improved abilities and alignment, and suggests metacognitive performance as an effective RL signal to overcome limits of prior intrinsic feedback methods.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_bd9429f1e8e2dd0a195c7645