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.- Anthology ID:
- 2024.findings-emnlp.613
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10467–10484
- Language:
- URL:
- https://aclanthology.org/2024.findings-emnlp.613
- DOI:
- 10.18653/v1/2024.findings-emnlp.613
- Cite (ACL):
- Fengzhu Zeng, Wenqian Li, Wei Gao, and Yan Pang. 2024. Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10467–10484, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs (Zeng et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-emnlp.613.pdf