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RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models

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arxiv 2404.12065 v2 pith:33T7Y3NA submitted 2024-04-18 cs.CL cs.AIcs.CYcs.ETcs.MA

M. Abdul Khaliq , P. Chang , M. Ma , B. Pflugfelder , F. Miletić This is my paper
classification cs.CLcs.AIcs.CYcs.ETcs.MA
keywords multimodalreasoningclaimsfact-checkfact-checkinginformationlanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. XFacta: Contemporary, Real-World Dataset and Evaluation for Multimodal Misinformation Detection with Multimodal LLMs

    cs.CL 2025-08 conditional novelty 6.0 of 10

    XFacta is a new real-world, post-January-2024 multimodal misinformation dataset from X, and evaluations show that MLLM detectors need external evidence, especially image-to-text evidence, with multi-step reasoning per...

  2. Augmented Vision-Language Models: A Systematic Review

    cs.CL 2025-07 conditional novelty 5.0 of 10

    A structured taxonomy of inference-time augmentation techniques that connect vision-language models to external symbolic systems, tools, and knowledge sources.

  3. RAG Security and Privacy: Formalizing the Threat Model and Attack Surface

    cs.CR 2025-09 conditional novelty 3.0 of 10

    A formal RAG threat model is defined with four adversary classes and game-based notions of membership inference, leakage, and poisoning, but the definitions largely restate known concepts and the main DP-based protect...

  4. Empowering Multimodal LLMs with External Tools: A Comprehensive Survey

    cs.CV 2025-08 unverdicted novelty 2.0 of 10

    A survey paper maps how external tools are used to augment multimodal large language models across data, tasks, evaluation, and future directions.

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