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arXiv · 2506.18274

Leveraging Large Language Models for Information Verification -- an Engineering Approach

Nguyen Nang Hung·Nguyen Thanh Trong·Vuong Thanh Toan·Nguyen An Phuoc·Dao Minh Tu·Nguyen Manh Duc Tuan·Nguyen Dinh Mau
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Abstract

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.

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Nguyen Nang Hung, Nguyen Thanh Trong, Vuong Thanh Toan, Nguyen An Phuoc, Dao Minh Tu, Nguyen Manh Duc Tuan, Nguyen Dinh Mau. 2025-06-23. Leveraging Large Language Models for Information Verification -- an Engineering Approach. https://arxiv.org/abs/2506.18274

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