Large vision-language models (LVLMs) have significantly improved multimodal
reasoning tasks, such as visual question answering and image captioning. These
models embed multimodal facts within their parameters, rather than relying on
external knowledge bases to store factual information explicitly. However, the
content discerned by LVLMs may deviate from actual facts due to inherent bias
or incorrect inference. To address this issue, we introduce MFC-Bench, a
rigorous and comprehensive benchmark designed to evaluate the factual accuracy
of LVLMs across three tasks: Manipulation, Out-of-Context, and Veracity
Classification. Through our evaluation on MFC-Bench, we benchmarked 12 diverse
and representative LVLMs, uncovering that current models still fall short in
multimodal fact-checking and demonstrate insensitivity to various forms of
manipulated content. We hope that MFC-Bench could raise attention to the
trustworthy artificial intelligence potentially assisted by LVLMs in the
future. The MFC-Bench and accompanying resources are publicly accessible at
this https URL, contributing to ongoing research in the
multimodal fact-checking field.

通过评估 MFC-Bench 对 12 个各异而代表性的大型视觉 - 语言模型进行了基准测试，发现当前模型在多模态事实核查方面仍存在不足，并对各种形式的操纵内容表现出麻木，希望通过 MFC-Bench 能够引起对未来可能由大型视觉 - 语言模型辅助的可信人工智能的关注。