@inproceedings{zeng-etal-2024-multimodal,
title = "Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal {LLM}s",
author = "Zeng, Fengzhu and
Li, Wenqian and
Gao, Wei and
Pang, Yan",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.613",
doi = "10.18653/v1/2024.findings-emnlp.613",
pages = "10467--10484",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs
%A Zeng, Fengzhu
%A Li, Wenqian
%A Gao, Wei
%A Pang, Yan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zeng-etal-2024-multimodal
%X 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.
%R 10.18653/v1/2024.findings-emnlp.613
%U https://aclanthology.org/2024.findings-emnlp.613
%U https://doi.org/10.18653/v1/2024.findings-emnlp.613
%P 10467-10484
Markdown (Informal)
[Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs](https://aclanthology.org/2024.findings-emnlp.613) (Zeng et al., Findings 2024)
ACL