  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foundation model training data becoming outdated.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
inter- domain benchmarks or explanations generated from large language models
(LLMs). We evaluate on 12 public benchmarks for fact-checking and
misinformation detection as well as two other tasks relevant to content
moderation -- toxicity and stance detection. Our results on two recent
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%.
