Multimodal large language models (MLLMs) carry the potential to support
humans in processing vast amounts of information. While MLLMs are already being
used as a fact-checking tool, their abilities and limitations in this regard
are understudied. Here is aim to bridge this gap. In particular, we propose a
framework for systematically assessing the capacity of current multimodal
models to facilitate real-world fact-checking. Our methodology is
evidence-free, leveraging only these models' intrinsic knowledge and reasoning
capabilities. By designing prompts that extract models' predictions,
explanations, and confidence levels, we delve into research questions
concerning model accuracy, robustness, and reasons for failure. We empirically
find that (1) GPT-4V exhibits superior performance in identifying malicious and
misleading multimodal claims, with the ability to explain the unreasonable
aspects and underlying motives, and (2) existing open-source models exhibit
strong biases and are highly sensitive to the prompt. Our study offers insights
into combating false multimodal information and building secure, trustworthy
multimodal models. To the best of our knowledge, we are the first to evaluate
MLLMs for real-world fact-checking.

对多模态大型语言模型在事实检查方面的能力和局限性进行了系统评估，发现 GPT-4V 在识别恶意和误导性多模态论断方面表现出优越性能，具备解释不合理方面和潜在动机的能力，同时已有的开源模型存在强烈的偏见，并对提示非常敏感。这项研究为对抗虚假多模态信息和构建安全可靠的多模态模型提供了启示，据我们所知，这是第一次对多模态大型语言模型进行真实世界事实检查的评估。