  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.
