RAGAR, Your Falsehood RADAR: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models
Mohammed Abdul Khaliq, MSc. Computational Linguistics Programm at the Institute for Natural Language Processing of the University of Stuttgart, will give a talk on the topic of his Master’s thesis: “RAGAR, Your Falsehood RADAR: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models”.

Abstract

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. Our approaches improve the accuracy of veracity predictions and the generation of explanations over current fact-checking approaches by up to 15-17 %. By employing multimodal LLMs adept at analyzing both text and images, this research advances the capability of automated systems in identifying and countering misinformation.

In this series →

Large Language Models
Pills: CLP-Transfer: Cross-Lingual and … Pills: Position: Leverage Foundational Models … Pills: WECHSEL: Cross-Lingual Transfer