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.

翻译：多模态大语言模型（MLLMs）具备帮助人类处理海量信息的潜力。尽管MLLMs已被用作事实核查工具，但对其在此方面的能力与局限性研究尚不充分。本文旨在填补这一空白。具体而言，我们提出了一个系统性评估框架，用于衡量当前多模态模型支持真实世界事实核查的能力。该方法不依赖外部证据，仅利用模型的内在知识与推理能力。通过设计提示词提取模型的预测结果、解释信息及置信度级别，我们深入研究了模型准确性、鲁棒性及失败原因等研究问题。实证发现：（1）GPT-4V在识别恶意及误导性多模态声明方面表现优异，能够解释其中的不合理之处与潜在动机；（2）现有开源模型存在显著偏见，且对提示词高度敏感。本研究为打击虚假多模态信息、构建安全可信的多模态模型提供了见解。据我们所知，这是首次对MLLMs在真实世界事实核查中的表现进行评估。