这篇论文给出一个简单检测幻觉的方法，只靠模型内部的隐藏状态算一个分数，不用外部工具，在FAVA和RAG上验证有效。
论文提出D-Score，一种基于隐藏激活矩阵奇异值分布的谱统计量，通过单次前向传播计算。在FAVA-Annotation和RAGTruth两个基准上评估，以特定层和容忍参数下奇异值接近最大值的数量作为幻觉分数。实验表明D-Score是强隐藏状态信号，无需外部验证器、检索步骤或多轮生成即可检测幻觉。该方法灵感源于模型处理与内部知识冲突的文本时，隐藏表示会沿额外奇异方向扩散。
D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models
Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to be internally represented by the model. We study hallucination detection from the geometry of hidden activations and introduce the D-Score, a simple spectral statistic computed from a single forward pass. For a fixed model, layer, and tolerance parameter, the D-Score counts how many singular directions of the hidden activation matrix have singular values that remain close to the leading one. We use this quantity as a hallucination score, classifying an input text as hallucinated when its D-Score is larger than a pre-defined quantity. The motivation is that, when a model processes a text that conflicts with information available in its own internal state, the hidden representation may encode both the asserted content and some form of counter-evidence, uncertainty, correction, or lack of support; this can make the hidden trajectory spread across additional singular directions. We formalize this intuition through a lightweight spectral argument and evaluate the resulting detector on FAVA-Annotation and RAGTruth. The experiments indicate that the D-Score is a strong hidden-state signal for hallucination detection, while requiring no external verifier, no retrieval step, and no multiple generations.