Multimodal Large Language Models to Support Real-World Fact-Checking
Jiahui Geng Yova Kementchedjhieva Preslav Nakov Iryna Gurevych
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.