  Event Factuality Detection (EFD) task determines the factuality of textual
events, i.e., classifying whether an event is a fact, possibility, or
impossibility, which is essential for faithfully understanding and utilizing
event knowledge. However, due to the lack of high-quality large-scale data,
event factuality detection is under-explored in event understanding research,
which limits the development of EFD community. To address these issues and
provide faithful event understanding, we introduce MAVEN-Fact, a large-scale
and high-quality EFD dataset based on the MAVEN dataset. MAVEN-Fact includes
factuality annotations of 112,276 events, making it the largest EFD dataset.
Extensive experiments demonstrate that MAVEN-Fact is challenging for both
conventional fine-tuned models and large language models (LLMs). Thanks to the
comprehensive annotations of event arguments and relations in MAVEN, MAVEN-Fact
also supports some further analyses and we find that adopting event arguments
and relations helps in event factuality detection for fine-tuned models but
does not benefit LLMs. Furthermore, we preliminarily study an application case
of event factuality detection and find it helps in mitigating event-related
hallucination in LLMs. Our dataset and codes can be obtained from
\url{https://github.com/lcy2723/MAVEN-FACT}
