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Multi-modal fact verification has become an important but challenging issueon social media due to the mismatch between the text and images in themisinformation of news content, which has been addressed by consideringcross-modalities to identify the veracity of the news in recent years. In thispaper, we propose the Pre-CoFactv2 framework with new parameter-efficientfoundation models for modeling fine-grained text and input embeddings withlightening parameters, multi-modal multi-type fusion for not only capturingrelations for the same and different modalities but also for different types(i.e., claim and document), and feature representations for explicitlyproviding metadata for each sample. In addition, we introduce a unifiedensemble method to boost model performance by adjusting the importance of eachtrained model with not only the weights but also the powers. Extensiveexperiments show that Pre-CoFactv2 outperforms Pre-CoFact by a large margin andachieved new state-of-the-art results at the Factify challenge at AAAI 2023. Wefurther illustrate model variations to verify the relative contributions ofdifferent components. Our team won the first prize (F1-score: 81.82%) and wemade our code publicly available athttps://github.com/wwweiwei/Pre-CoFactv2-AAAI-2023.

Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification