用大模型自动化验证多媒体新闻真伪，流程高效可复现。
Leveraging Large Language Models for Information Verification -- an Engineering Approach
- 用GPT-4o驱动全流程，通过提示工程实现自动处理
- 从多媒体中提取关键帧与元数据，比对一致性发现矛盾
- 适合需要快速验证信息真实性的媒体机构或平台
针对ACMMM25挑战，我们提出一种面向多媒体新闻源验证的实用工程方法，以GPT-4o等大语言模型为核心构建流水线。流程包括：首先通过Google工具对图像和视频生成通用查询的元数据，获取相关内容与链接；接着将多媒体数据分割、清洗并转换为帧，从中选取Top-K张最具信息量的帧；这些帧与元数据交叉比对，识别共识或差异；同时提取音频转录文本用于进一步验证。整个流程由GPT-4o通过提示工程实现自动化，人工干预仅限最终审核。该方法具备高效率与可扩展性，适用于大规模新闻真实性核查场景。
原文摘要 · Abstract (English)
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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