多智能体框架提升健康伪信息辟谣内容的准确性与可信度
Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation
- 用多个大模型分工协作，分别负责查证、增强证据和优化回复
- 在多项指标上优于基线方法，尤其在事实准确性和相关性上表现突出
- 适合需要高可信度辟谣内容的平台或公共健康传播场景
将大型语言模型（LLMs）与检索增强生成（RAG）结合，在应对健康类虚假信息时展现出强大潜力。然而现有研究依赖有限证据，对生成结果控制不足。为此，本文提出一种多智能体检索增强框架，通过多个大模型协同优化知识检索、证据强化和回应精炼。该方法融合静态与动态证据，确保生成的辟谣内容具备相关性、充分依据且及时更新。实验表明，本方法在礼貌性、相关性、信息量和事实准确性上均优于基线模型。消融实验验证了各组件必要性，跨数据集评估显示系统在多种健康伪信息主题上具有良好泛化能力。人工评估进一步证明，经过精炼后的回应显著提升质量并更受人类偏好。
原文摘要 · Abstract (English)
Large language models (LLMs) incorporated with Retrieval-Augmented Generation (RAG) have demonstrated powerful capabilities in generating counterspeech against misinformation. However, current studies rely on limited evidence and offer less control over final outputs. To address these challenges, we propose a Multi-agent Retrieval-Augmented Framework to generate counterspeech against health misinformation, incorporating multiple LLMs to optimize knowledge retrieval, evidence enhancement, and response refinement. Our approach integrates both static and dynamic evidence, ensuring that the generated counterspeech is relevant, well-grounded, and up-to-date. Our method outperforms baseline approaches in politeness, relevance, informativeness, and factual accuracy, demonstrating its effectiveness in generating high-quality counterspeech. To further validate our approach, we conduct ablation studies to verify the necessity of each component in our framework. Furthermore, cross evaluations show that our system generalizes well across diverse health misinformation topics and datasets. And human evaluations reveal that refinement significantly enhances counterspeech quality and obtains human preference.
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