Summary of Multimodal Large Language Models to Support Real-world Fact-checking, by Jiahui Geng et al.
Multimodal Large Language Models to Support Real-World Fact-Checking
by Jiahui Geng, Yova Kementchedjhieva, Preslav Nakov, Iryna Gurevych
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper aims to investigate the potential of multimodal large language models (MLLMs) in supporting human fact-checking efforts. While MLLMs have been used as fact-checking tools, their capabilities and limitations in this regard are understudied. The authors propose a framework for systematically assessing the capacity of current multimodal models to facilitate real-world fact-checking. They design prompts that extract models’ predictions, explanations, and confidence levels, delving into research questions concerning model accuracy, robustness, and reasons for failure. The study finds that GPT-4V exhibits superior performance in identifying malicious and misleading multimodal claims, with the ability to explain unreasonable aspects and underlying motives. However, existing open-source models exhibit strong biases and are highly sensitive to prompts. The authors’ findings offer insights into combating false multimodal information and building secure, trustworthy MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computers can help humans check if what we read is true or not. Right now, these computer models are being used to fact-check information, but nobody really knows how good they are at it. The authors of this paper came up with a way to test these computer models and see how well they do. They made special questions that the computers can answer, which gives them clues about what the computers think is true or not. What they found out is that one type of computer model called GPT-4V is really good at identifying false information and can even explain why it thinks something is false. However, other types of computer models are not as good and can be tricked into thinking false things are true. This study helps us understand how to make these computer models better at fact-checking and more trustworthy. |
Keywords
» Artificial intelligence » Gpt
