The rapid growth of digital communication platforms has significantly increased the spread of misinformation and fake news across the internet. With the emergence of generative artificial intelligence and large language models (LLMs), fake content has become more sophisticated and difficult to detect using traditional verification methods. Manual fact-checking performed by journalists and verification organizations is reliable but time-consuming and cannot scale to the massive volume of online information. This paper proposes a Fact Check News Verification Platform that integrates Large Language Models with Retrieval-Augmented Generation (RAG) to automatically verify the authenticity of news claims. The system extracts factual claims from news articles, retrieves supporting evidence from credible sources, and evaluates the claim using LLM-based reasoning. The architecture includes claim extraction, semantic retrieval, evidence aggregation, and credibility classification modules. By grounding the responses of language models in retrieved external knowledge, the proposed system reduces hallucination and improves the reliability of automated fact verification. The platform demonstrates how combining modern natural language processing techniques with retrieval systems can create scalable fact-checking solutions capable of combating misinformation in digital media ecosystems.
P et al. (Thu,) studied this question.