For the ACMMM25 challenge, we present a practical engineering approach to multimedia news source verification, utilizing Large Language Models (LLMs) like GPT-4o as the backbone of our pipeline. Our method processes images and videos through a streamlined sequence of steps: First, we generate metadata using general-purpose queries via Google tools, capturing relevant content and links. Multimedia data is then segmented, cleaned, and converted into frames, from which we select the top-K most informative frames. These frames are cross-referenced with metadata to identify consensus or discrepancies. Additionally, audio transcripts are extracted for further verification. Noticeably, the entire pipeline is automated using GPT-4o through prompt engineering, with human intervention limited to final validation.
@article{arxiv.2506.18274,
title = {Leveraging Large Language Models for Information Verification -- an Engineering Approach},
author = {Nguyen Nang Hung and Nguyen Thanh Trong and Vuong Thanh Toan and Nguyen An Phuoc and Dao Minh Tu and Nguyen Manh Duc Tuan and Nguyen Dinh Mau},
journal= {arXiv preprint arXiv:2506.18274},
year = {2025}
}