  The popularity of automated news headline generation has surged with
advancements in pre-trained language models. However, these models often suffer
from the ``hallucination'' problem, where the generated headline is not fully
supported by its source article. Efforts to address this issue have
predominantly focused on English, using over-simplistic classification schemes
that overlook nuanced hallucination types. In this study, we introduce the
first multilingual, fine-grained news headline hallucination detection dataset
that contains over 11 thousand pairs in 5 languages, each annotated with
detailed hallucination types by experts. We conduct extensive experiments on
this dataset under two settings. First, we implement several supervised
fine-tuning approaches as preparatory solutions and demonstrate this dataset's
challenges and utilities. Second, we test various large language models'
in-context learning abilities and propose two novel techniques,
language-dependent demonstration selection and coarse-to-fine prompting, to
boost the few-shot hallucination detection performance in terms of the
example-F1 metric. We release this dataset to foster further research in
multilingual, fine-grained headline hallucination detection.
