FinVet用多智能体协作检测金融假消息，结果可溯源、有置信度。
FinVet: A Collaborative Framework of RAG and External Fact-Checking Agents for Financial Misinformation Detection
- 融合RAG与外部验真，通过置信度加权投票动态调整验证策略。
- 在FinFact数据集上F1达0.85，比最佳单模型提升10.4%。
- 适合金融风控、监管机构，提供证据链和不确定性提示。
金融市场正面临日益严重的虚假信息威胁，可能在几分钟内造成数十亿美元损失。现有方法大多决策过程不透明，且缺乏可信来源的归因。我们提出FinVet，一种新型多智能体框架，整合两条检索增强生成（RAG）管道，并通过外部验真实现置信度加权投票。FinVet采用自适应三阶段处理机制，根据检索置信度动态调整验证策略：从直接元数据提取，到混合推理，再到全模型分析。与现有方法不同，FinVet提供基于证据的判断、来源归因、置信度评分以及证据不足时的显式不确定性标记。在FinFact数据集上的实验表明，FinVet达到0.85的F1分数，较最优单个管道（验真管道）提升10.4%，较独立RAG方法提升37%。
原文摘要 · Abstract (English)
Financial markets face growing threats from misinformation that can trigger billions in losses in minutes. Most existing approaches lack transparency in their decision-making and provide limited attribution to credible sources. We introduce FinVet, a novel multi-agent framework that integrates two Retrieval-Augmented Generation (RAG) pipelines with external fact-checking through a confidence-weighted voting mechanism. FinVet employs adaptive three-tier processing that dynamically adjusts verification strategies based on retrieval confidence, from direct metadata extraction to hybrid reasoning to full model-based analysis. Unlike existing methods, FinVet provides evidence-backed verdicts, source attribution, confidence scores, and explicit uncertainty flags when evidence is insufficient. Experimental evaluation on the FinFact dataset shows that FinVet achieves an F1 score of 0.85, which is a 10.4% improvement over the best individual pipeline (fact-check pipeline) and 37% improvement over standalone RAG approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。