RAMA：用于多模态事实核查中虚假信息检测的检索增强多智能体框架
RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking
AI总结：
提出检索增强多智能体框架RAMA，通过策略性查询生成、交叉验证证据聚合与多智能体集成架构，利用多模态大语言模型和检索事实证据，有效解决多模态虚假信息核查中模糊声明验证难题，提升系统可靠性与可扩展性。
摘要：
多模态虚假信息的快速泛滥给自动化事实核查系统带来了重大挑战，特别是当声明模糊或缺乏足够上下文时。我们提出了RAMA，一个专为验证多媒体虚假信息而设计的检索增强多智能体框架。RAMA包含三项核心创新：（1）策略性查询生成，将多模态声明转化为精确的网络搜索查询；（2）交叉验证证据聚合，从多样且权威的来源中收集证据；（3）多智能体集成架构，利用多个多模态大语言模型和提示变体的互补优势。大量实验表明，RAMA在基准数据集上取得了卓越性能，尤其擅长通过将验证基于检索到的事实证据来解决模糊或不太可能的声明。我们的发现强调了整合网络证据和多智能体推理对于可信多媒体验证的必要性，为更可靠、可扩展的事实核查解决方案铺平了道路。RAMA将在 https://github.com/kalendsyang/RAMA.git 公开提供。
英文摘要：
The rapid proliferation of multimodal misinformation presents significant challenges for automated fact-checking systems, especially when claims are ambiguous or lack sufficient context. We introduce RAMA, a novel retrieval-augmented multi-agent framework designed for verifying multimedia misinformation. RAMA incorporates three core innovations: (1) strategic query formulation that transforms multimodal claims into precise web search queries; (2) cross-verification evidence aggregation from diverse, authoritative sources; and (3) a multi-agent ensemble architecture that leverages the complementary strengths of multiple multimodal large language models and prompt variants. Extensive experiments demonstrate that RAMA achieves superior performance on benchmark datasets, particularly excelling in resolving ambiguous or improbable claims by grounding verification in retrieved factual evidence. Our findings underscore the necessity of integrating web-based evidence and multi-agent reasoning for trustworthy multimedia verification, paving the way for more reliable and scalable fact-checking solutions. RAMA will be publicly available at https://github.com/kalendsyang/RAMA.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
