用图结构提升大模型不确定性估计，更准更可靠。
GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models
- 构建依赖句法图，融合层级图池化捕捉文本结构关系。
- 在多个任务上比语义熵方法提升29%的AUROC，校准误差降低15%以上。
- 适合需要高可信度输出的医疗、金融等关键领域应用。
不确定性估计对提升大语言模型在高风险场景下的可靠性至关重要。现有方法多忽略语义依赖，仅依赖词元级概率，难以捕捉生成文本中的结构关系。本文提出GENUINE：一种基于图的多层级不确定性估计框架，利用依存句法树与层次图池化技术，增强对语义和结构关系的建模能力。通过监督学习，该方法显著提升了置信度评估的准确性。在多个NLP任务上的实验表明，GENUINE相较基于语义熵的方法，最高可提升29%的AUROC，并将校准误差降低超过15%，验证了图结构在不确定性建模中的有效性。代码已开源：https://github.com/ODYSSEYWT/GUQ。
原文摘要 · Abstract (English)
Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semantic dependencies, relying on token-level probability measures that fail to capture structural relationships within the generated text. We propose GENUINE: Graph ENhanced mUlti-level uncertaINty Estimation for Large Language Models, a structure-aware framework that leverages dependency parse trees and hierarchical graph pooling to refine uncertainty quantification. By incorporating supervised learning, GENUINE effectively models semantic and structural relationships, improving confidence assessments. Extensive experiments across NLP tasks show that GENUINE achieves up to 29% higher AUROC than semantic entropy-based approaches and reduces calibration errors by over 15%, demonstrating the effectiveness of graph-based uncertainty modeling. The code is available at https://github.com/ODYSSEYWT/GUQ.
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