一种新颖的幻觉分类框架
A novel hallucination classification framework
AI总结：
本文提出一种基于分类法和受控复现的幻觉自动检测框架，通过嵌入与无监督学习证明简单分类算法可有效区分LLM幻觉与正确响应。
摘要：
本工作引入了一种新颖的方法论，用于自动检测大型语言模型（LLM）推理过程中产生的幻觉。所提出的方法基于系统性的分类法，并通过提示工程对多种幻觉类型进行受控复现。随后，一个专门的幻觉数据集通过嵌入模型映射到向量空间，并利用无监督学习技术在降维表示中进行分析，该表示将幻觉与真实响应并列。对质心间距离的定量评估揭示了幻觉中信息失真严重程度与其相对于正确输出簇的空间偏离之间存在一致的相关性。这些发现提供了理论和实证证据，表明即使简单的分类算法也能可靠地区分单个LLM内的幻觉与准确响应，从而为提升模型可靠性提供了一种轻量级且有效的框架。
英文摘要：
This work introduces a novel methodology for the automatic detection of hallucinations generated during large language model (LLM) inference. The proposed approach is based on a systematic taxonomy and controlled reproduction of diverse hallucination types through prompt engineering. A dedicated hallucination dataset is subsequently mapped into a vector space using an embedding model and analyzed with unsupervised learning techniques in a reduced-dimensional representation of hallucinations with veridical responses. Quantitative evaluation of inter-centroid distances reveals a consistent correlation between the severity of informational distortion in hallucinations and their spatial divergence from the cluster of correct outputs. These findings provide theoretical and empirical evidence that even simple classification algorithms can reliably distinguish hallucinations from accurate responses within a single LLM, thereby offering a lightweight yet effective framework for improving model reliability.
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