Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs

• URL: http://arxiv.org/abs/2409.19656v1
• Date: Sun, 29 Sep 2024 11:01:14 GMT
• Title: Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs
• Authors: Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang,
• Abstract summary: We propose learning from synthetic data for detecting real-world multimodal misinformation through two model-agnostic data selection methods. Experiments show that our method enhances the performance of a small MLLM on real-world fact-checking datasets.
• Score: 13.684959490938269
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is costly, leading researchers to use synthetic datasets generated by AI technologies. However, the generalizability of detectors trained on synthetic data to real-world scenarios remains unclear due to the distribution gap. To address this, we propose learning from synthetic data for detecting real-world multimodal misinformation through two model-agnostic data selection methods that match synthetic and real-world data distributions. Experiments show that our method enhances the performance of a small MLLM (13B) on real-world fact-checking datasets, enabling it to even surpass GPT-4V~\cite{GPT-4V}.

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