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

arXiv: https://arxiv.org/abs/2404.12065 · HTML: https://mlsanity.com/paper/2404.12065/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)
- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 8 of 8)

- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023)
- [Chain-of-Verification Reduces Hallucination in Large Language Models](https://mlsanity.com/paper/2309.11495/) (2023)
- [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://mlsanity.com/paper/2307.13528/) (2023)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks](https://mlsanity.com/paper/2304.14732/) (2023)
- [JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims](https://mlsanity.com/paper/2401.08026/) (2024)
- [Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models](https://mlsanity.com/paper/2403.17359/) (2024)

## Built on by (most cited first, 27 of 27)

- [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://mlsanity.com/paper/2505.04921/) (2025)
- [Synthetic Data Generation Using Large Language Models: Advances in Text and Code](https://mlsanity.com/paper/2503.14023/) (2025)
- [DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts](https://mlsanity.com/paper/2412.10510/) (2024)
- [The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2502.20995/) (2025)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation](https://mlsanity.com/paper/2508.20131/) (2025)
- [RAG Security and Privacy: Formalizing the Threat Model and Attack Surface](https://mlsanity.com/paper/2509.20324/) (2025)
- [XFacta: Contemporary, Real-World Dataset and Evaluation for Multimodal Misinformation Detection with Multimodal LLMs](https://mlsanity.com/paper/2508.09999/) (2025)
- [The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers](https://mlsanity.com/paper/2506.20844/) (2025)
- [Towards Geo-Culturally Grounded LLM Generations](https://mlsanity.com/paper/2502.13497/) (2025)
- [T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search](https://mlsanity.com/paper/2505.19768/) (2025)
- [MAD-Fact: A Multi-Agent Debate Framework for Long-Form Factuality Evaluation in LLMs](https://mlsanity.com/paper/2510.22967/) (2025)
- [OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography](https://mlsanity.com/paper/2409.09052/) (2024)
- [Task-Oriented Automatic Fact-Checking with Frame-Semantics](https://mlsanity.com/paper/2501.13288/) (2025)
- [Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning](https://mlsanity.com/paper/2505.13994/) (2025)
- [A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection](https://mlsanity.com/paper/2509.11687/) (2025)
- [MultiCheck: Strengthening Web Trust with Unified Multimodal Fact Verification](https://mlsanity.com/paper/2508.05097/) (2025)
- [MERIT: Modular Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning](https://mlsanity.com/paper/2510.17590/) (2025)
- [From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models](https://mlsanity.com/paper/2511.12861/) (2025)
- [Benchmarking Large Language Models for Knowledge Graph Validation](https://mlsanity.com/paper/2602.10748/) (2026)
- [Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval models](https://mlsanity.com/paper/2503.22877/) (2025)
- [Enhancing Critical Thinking with AI: A Tailored Warning System for RAG Models](https://mlsanity.com/paper/2504.16883/) (2025)
- [D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detection](https://mlsanity.com/paper/2602.14441/) (2026)
- [CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs](https://mlsanity.com/paper/2604.10502/) (2026)
- [CARO: Chain-of-Analogy Reasoning Optimization for Robust Content Moderation](https://mlsanity.com/paper/2604.10504/) (2026)
- [PURPOSE: Poisoning Conflict Resolution in RAG via Proxy-Fact-Grounded Updates](https://mlsanity.com/paper/2608.04756/) (2026)
- [To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech](https://mlsanity.com/paper/2609.30227/) (2026)

## Abstract (arXiv)

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.