Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Existing hallucination detection methods achieve strong performance when the training and test data come from the same domain, but they suffer from poor cross-domain generalization. In this paper, we study an important yet overlooked problem, termed generalizable hallucination detection (GHD), which aims to train hallucination detectors on data from a single domain while ensuring robust performance across diverse related domains. In studying GHD, we simulate multi-turn dialogues following LLMs' initial response and observe an interesting phenomenon: hallucination-initiated multi-turn dialogues universally exhibit larger uncertainty fluctuations than factual ones across different domains. Based on the phenomenon, we propose a new score SpikeScore, which quantifies abrupt fluctuations in multi-turn dialogues. Through both theoretical analysis and empirical validation, we demonstrate that SpikeScore achieves strong cross-domain separability between hallucinated and non-hallucinated responses. Experiments across multiple LLMs and benchmarks demonstrate that the SpikeScore-based detection method outperforms representative baselines in cross-domain generalization and surpasses advanced generalization-oriented methods, verifying the effectiveness of our method in cross-domain hallucination detection.

翻译：幻觉检测对于在现实应用中部署大型语言模型至关重要。现有幻觉检测方法在训练和测试数据来自相同领域时表现优异，但在跨域泛化方面表现不佳。本文研究了一个重要但被忽视的问题，即泛化性幻觉检测，其目标是在单一领域数据上训练幻觉检测器，同时确保在不同相关领域中具有鲁棒性能。在研究GHD时，我们模拟了大型语言模型初始响应后的多轮对话，并观察到一个有趣现象：在不同领域中，幻觉引发的多轮对话普遍比事实性对话表现出更大的不确定性波动。基于此现象，我们提出了一种新评分方法SpikeScore，用于量化多轮对话中的突发波动。通过理论分析和实证验证，我们证明SpikeScore在幻觉与非幻觉响应之间实现了强大的跨域可分离性。在多个大型语言模型和基准测试上的实验表明，基于SpikeScore的检测方法在跨域泛化方面优于代表性基线，并超越了先进的面向泛化方法，验证了我们的方法在跨域幻觉检测中的有效性。