标题： 利用语义图进行幻觉检测的不确定性建模增强 显示英文标题

标题： Enhancing Uncertainty Modeling with Semantic Graph for Hallucination Detection

摘要： 大型语言模型（LLMs）容易产生非事实性或不忠实的陈述，这会削弱其在现实场景中的应用。 最近的研究集中在基于不确定性的幻觉检测，这种方法利用LLMs的输出概率进行不确定度计算，并不依赖外部知识或频繁地从LLMs中采样。 然而，大多数方法仅考虑每个独立标记的不确定性，而标记和句子之间的复杂语义关系尚未得到充分研究，这限制了对跨越多个标记和句子的幻觉的检测。 在本文中，我们提出了一种方法，通过语义图来增强不确定度建模以进行幻觉检测。 具体来说，我们首先构建一个能够很好地捕捉实体标记和句子之间关系的语义图。 然后，我们将两个实体之间的关系引入不确定度传播，以增强句子级别的幻觉检测。 鉴于幻觉是由于句子之间的冲突引起的，我们进一步提出了一种基于图的不确定度校准方法，该方法将句子与其在语义图中的邻居之间的矛盾概率结合起来进行不确定度计算。 在两个数据集上的大量实验表明了我们所提出方法的巨大优势。 特别是，在段落级别的幻觉检测中，我们获得了19.78%的显著提升。 显示英文摘要

摘要： Large Language Models (LLMs) are prone to hallucination with non-factual or unfaithful statements, which undermines the applications in real-world scenarios. Recent researches focus on uncertainty-based hallucination detection, which utilizes the output probability of LLMs for uncertainty calculation and does not rely on external knowledge or frequent sampling from LLMs. Whereas, most approaches merely consider the uncertainty of each independent token, while the intricate semantic relations among tokens and sentences are not well studied, which limits the detection of hallucination that spans over multiple tokens and sentences in the passage. In this paper, we propose a method to enhance uncertainty modeling with semantic graph for hallucination detection. Specifically, we first construct a semantic graph that well captures the relations among entity tokens and sentences. Then, we incorporate the relations between two entities for uncertainty propagation to enhance sentence-level hallucination detection. Given that hallucination occurs due to the conflict between sentences, we further present a graph-based uncertainty calibration method that integrates the contradiction probability of the sentence with its neighbors in the semantic graph for uncertainty calculation. Extensive experiments on two datasets show the great advantages of our proposed approach. In particular, we obtain substantial improvements with 19.78% in passage-level hallucination detection.