RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models
<|reference_start|>RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models: The escalating challenge of misinformation, particularly in political discourse, requires advanced fact-checking solutions; this is even clearer in the more complex scenario of multimodal claims. We tackle this issue using a multimodal large language model in conjunction with retrieval-augmented generation (RAG), and introduce two novel reasoning techniques: Chain of RAG (CoRAG) and Tree of RAG (ToRAG). They fact-check multimodal claims by extracting both textual and image content, retrieving external information, and reasoning subsequent questions to be answered based on prior evidence. We achieve a weighted F1-score of 0.85, surpassing a baseline reasoning technique by 0.14 points. Human evaluation confirms that the vast majority of our generated fact-check explanations contain all information from gold standard data.<|reference_end|>
@article{khaliq2024ragar,,
    title={RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political
  Fact-Checking using Multimodal Large Language Models},
    author={M. Abdul Khaliq, P. Chang, M. Ma, B. Pflugfelder, F. Mileti'c},
    journal={arXiv preprint arXiv:2404.12065},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12065},
    primaryClass={cs.CL cs.AI cs.CY cs.ET cs.MA}
}