RAGAR, Your Falsehood RADAR: RAG-Augmented Reasoning for Political
  Fact-Checking using Multimodal Large Language Models
M. Abdul Khaliq P. Chang M. Ma B. Pflugfelder F. Miletić
The escalating challenge of misinformation, particularly in the context of
political discourse, necessitates advanced solutions for fact-checking. We
introduce innovative approaches to enhance the reliability and efficiency of
multimodal fact-checking through the integration of Large Language Models
(LLMs) with Retrieval-augmented Generation (RAG)- based advanced reasoning
techniques. This work proposes two novel methodologies, Chain of RAG (CoRAG)
and Tree of RAG (ToRAG). The approaches are designed to handle multimodal
claims by reasoning the next questions that need to be answered based on
previous evidence. Our approaches improve the accuracy of veracity predictions
and the generation of explanations over the traditional fact-checking approach
of sub-question generation with chain of thought veracity prediction. By
employing multimodal LLMs adept at analyzing both text and images, this
research advances the capability of automated systems in identifying and
countering misinformation.