While Large language models (LLMs) have garnered widespread applications
across various domains due to their powerful language understanding and
generation capabilities, the detection of non-factual or hallucinatory content
generated by LLMs remains scarce. Currently, one significant challenge in
hallucination detection is the laborious task of time-consuming and expensive
manual annotation of the hallucinatory generation. To address this issue, this
paper first introduces a method for automatically constructing model-specific
hallucination datasets based on existing fact-checking datasets called
AutoHall. Furthermore, we propose a zero-resource and black-box hallucination
detection method based on self-contradiction. We conduct experiments towards
prevalent open-/closed-source LLMs, achieving superior hallucination detection
performance compared to extant baselines. Moreover, our experiments reveal
variations in hallucination proportions and types among different models.

该论文提出了 AutoHall 方法，通过自相矛盾的方式自动构建模型特定的幻觉数据集，然后基于这些数据集实现了无资源和黑盒幻觉检测方法，对开源和闭源大型语言模型进行了实验证明，在幻觉检测性能上优于现有基准模型，并且发现了不同模型之间的幻觉比例和类型的差异。