用模型内部状态提升不确定性估计，让大模型更清楚自己知道多少。
Enhancing Uncertainty Estimation in LLMs with Expectation of Aggregated Internal Belief
- 从多个中间层提取内部信念，再取期望值生成更准的置信度
- 在多个数据集上显著改善模型校准性能，降低过自信率
- 适合需要可靠不确定性的安全应用，如医疗、金融决策
大型语言模型在众多自然语言任务中表现卓越，但常表现出过度自信，生成看似合理却错误的答案。尤其经过人类反馈强化学习（RLHF）后，这种过自信问题更加严重，影响可靠性与安全部署。本文提出EAGLE（Expectation of AGgregated internal bEief），一种基于自评估的校准方法，利用模型内部隐藏状态生成更准确的置信度评分。不同于依赖最终输出的传统方法，EAGLE在自评估过程中提取多层中间状态中的内部信念，聚合后计算期望，得到更能反映模型真实确定性的置信度。在多种数据集和模型上的实验表明，该方法显著优于现有基线。我们还深入分析了各层不确定性模式、自评估提示的影响及评分范围的作用。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have achieved remarkable success across a wide range of natural language tasks, but often exhibit overconfidence and generate plausible yet incorrect answers. This overconfidence, especially in models undergone Reinforcement Learning from Human Feedback (RLHF), poses significant challenges for reliable uncertainty estimation and safe deployment. In this paper, we propose EAGLE (Expectation of AGgregated internaL bEief), a novel self-evaluation-based calibration method that leverages the internal hidden states of LLMs to derive more accurate confidence scores. Instead of relying on the model's final output, our approach extracts internal beliefs from multiple intermediate layers during self-evaluation. By aggregating these layer-wise beliefs and calculating the expectation over the resulting confidence score distribution, EAGLE produces a refined confidence score that more faithfully reflects the model's internal certainty. Extensive experiments on diverse datasets and LLMs demonstrate that EAGLE significantly improves calibration performance over existing baselines. We also provide an in-depth analysis of EAGLE, including a layer-wise examination of uncertainty patterns, a study of the impact of self-evaluation prompts, and an analysis of the effect of self-evaluation score range.
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