  Large Language Models (LLMs) have shown their ability to collaborate
effectively with humans in real-world scenarios. However, LLMs are apt to
generate hallucinations, i.e., makeup incorrect text and unverified
information, which can cause significant damage when deployed for
mission-critical tasks. In this paper, we propose a self-check approach based
on reverse validation to detect factual errors automatically in a zero-resource
fashion. To facilitate future studies and assess different methods, we
construct a hallucination detection benchmark named PHD, which is generated by
ChatGPT and annotated by human annotators. Contrasting previous studies of
zero-resource hallucination detection, our method and benchmark concentrate on
passage-level detection instead of sentence-level. We empirically evaluate our
method and existing zero-resource detection methods on two datasets. The
experimental results demonstrate that the proposed method considerably
outperforms the baselines while costing fewer tokens and less time.
Furthermore, we manually analyze some hallucination cases that LLM failed to
capture, revealing the shared limitation of zero-resource methods.
