Reliable uncertainty quantification (UQ) is essential for deploying large language models (LLMs) in safety-critical scenarios, as it enables them to abstain from responding when uncertain, thereby avoiding hallucinating falsehoods. However, state-of-the-art UQ methods primarily rely on semantic probability distributions or pairwise distances, overlooking latent semantic structural information that could enable more precise uncertainty estimates. This paper presents Semantic Structural Entropy (SeSE), a principled UQ framework that quantifies the inherent semantic uncertainty of LLMs from a structural information perspective for hallucination detection. Specifically, to effectively model semantic spaces, we first develop an adaptively sparsified directed semantic graph construction algorithm that captures directional semantic dependencies while automatically pruning unnecessary connections that introduce negative interference. We then exploit latent semantic structural information through hierarchical abstraction: SeSE is defined as the structural entropy of the optimal semantic encoding tree, formalizing intrinsic uncertainty within semantic spaces after optimal compression. A higher SeSE value corresponds to greater uncertainty, indicating that LLMs are highly likely to generate hallucinations. In addition, to enhance fine-grained UQ in long-form generation -- where existing methods often rely on heuristic sample-and-count techniques -- we extend SeSE to quantify the uncertainty of individual claims by modeling their random semantic interactions, providing theoretically explicable hallucination detection. Extensive experiments across 29 model-dataset combinations show that SeSE significantly outperforms advanced UQ baselines, including strong supervised methods and the recently proposed KLE.

翻译：可靠的不确定性量化对于在安全关键场景中部署大语言模型至关重要，因为它使模型能够在不确定时选择不回应，从而避免产生虚假幻觉。然而，现有最先进的不确定性量化方法主要依赖于语义概率分布或成对距离，忽略了潜在的语义结构信息，而这些信息可能实现更精确的不确定性估计。本文提出语义结构熵，这是一个基于结构信息视角量化大语言模型内在语义不确定性的原则性不确定性量化框架，用于幻觉检测。具体而言，为有效建模语义空间，我们首先开发了一种自适应稀疏化有向语义图构建算法，该算法在捕获方向性语义依赖的同时，自动剪枝引入负面干扰的不必要连接。随后，我们通过层次抽象利用潜在语义结构信息：语义结构熵被定义为最优语义编码树的结构熵，形式化地表达了语义空间在最优压缩后的内在不确定性。较高的语义结构熵值对应更大的不确定性，表明大语言模型极有可能产生幻觉。此外，为增强长文本生成中的细粒度不确定性量化——现有方法在此常依赖启发式采样计数技术——我们将语义结构熵扩展至通过建模随机语义交互来量化单个主张的不确定性，从而提供理论可解释的幻觉检测。在29个模型-数据集组合上的大量实验表明，语义结构熵显著优于先进的不确定性量化基线方法，包括强监督方法和近期提出的KLE方法。