How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models
Jae-Young Lee, Ximing Lu, Jack Hessel, Faeze Brahman, Youngjae Yu, Yonatan Bisk, Yejin Choi, Saadia Gabriel · 2024
Given the growing influx of misinformation across news and social media, there is a critical need for systems that can provide effective real-time verification of news claims.Large language or multimodal model based verification has been proposed to scale up online policing mechanisms for mitigating spread of false and harmful content.While these can potentially reduce burden on human fact-checkers, such efforts may be hampered by foundation model training data becoming outdated.In this work, we test the limits of improving foundation model performance without continual updating through an initial study of knowledge transfer using either existing intra-and interdomain benchmarks or explanations generated from large language models (LLMs).We evaluate open multimodal foundation models on twelve public benchmarks covering factchecking, misinformation, toxicity and stance detection.Our results on two recent and widely used multi-modal fact-checking benchmarks, Mocheg and Fakeddit, indicate that knowledge transfer strategies can improve Fakeddit performance over the state-of-the-art by up to 1.7% and Mocheg performance by up to 2.9%.The code, model checkpoints, and dataset are available: https://github.com/given131/ fact-verifier-knowledge-transfer.