这篇论文用多个开源模型合作检测假新闻，效果比GPT-4还好，还用了四种语言测试，值得搞安全或内容审核的人看看。
该论文提出一种多智能体虚假信息检测方法，模仿人类标注者的决策过程，采用共识机制、认知与知识多样性以及层次结构。系统使用开源模型LLaMA、Qwen、Deepseek等，确保透明度。在英语、波兰语、斯洛伐克语、保加利亚语数据集上评估，性能超过GPT-4和GPT-3.5。任务包括直接检测虚假信息、识别值得验证的文本、检测可验证事实主张。
Multi-Agentic System Leveraging Open-Source LLMs to Mitigate Disinformation Threats
In contemporary societies, the threat of disinformation has reached alarming levels, exacerbated by the proliferation of electronic communication, social media, and advancements in artificial intelligence. As a result, there is an urgent need to develop effective countermeasures to mitigate this menace. However, the sheer scale of the problem renders manual fact-checking and human-based verification inadequate, underscoring the necessity for automated methods to detect and debunk disinformation. This article proposes a novel approach based on a multi-agent system that emulates the decision-making processes of human annotators engaged in disinformation detection tasks. By incorporating a consensus mechanism, diversity in cognition and diversity in knowledge, and also hierarchical structure, inspired by human annotators' behavior, the proposed method achieves superior results compared to individual Large Language Models (LLMs), including GPT 4 and GPT 3.5. The system leverages open models (e.g., LLaMA, Kimi, Qwen, Deepseek and LLaMA-Nemotron) to ensure greater transparency. The evaluation of the proposed method encompasses datasets in languages with varying resource availability, including English (high-resource), Polish (medium-resource), Slovak (low-resource) and Bulgarian (low-resource). Experiments were conducted on tasks such as direct disinformation detection, identification of texts worthy of verification, and detection of texts containing verifiable factual claims.