通过立场分离的多智能体辩论实现突发事件谣言检测
Breaking Event Rumor Detection via Stance-Separated Multi-Agent Debate
AI总结：
针对社交媒体突发事件谣言检测中缺乏标注资源、LLMs多样性有限的问题，提出立场分离的多智能体辩论模型S2MAD，通过分类评论立场和主张类型、多轮辩论及法官裁决，在两个数据集上性能优于现有方法，提升LLMs检测效果。
摘要：
社交媒体平台上突发事件期间谣言的快速传播严重阻碍了真相的传播。先前的研究表明，缺乏标注资源阻碍了对昨日新闻未涵盖的不可预见的突发事件的直接检测。利用大型语言模型（LLMs）进行谣言检测具有重大前景。然而，由于多样性有限，LLMs 难以对复杂或有争议的问题提供全面的回应。在这项工作中，我们提出了立场分离的多智能体辩论（S2MAD）来解决这个问题。具体来说，我们首先引入立场分离，将评论分为支持或反对原始主张。随后，将主张分为主观或客观，使智能体能够针对每种类型的主张使用不同的提示策略生成合理的初始观点。辩手随后遵循特定的指令进行多轮辩论以达成共识。如果未达成共识，法官智能体将评估意见并就主张的真实性做出最终裁决。在两个真实世界数据集上进行的大量实验表明，我们提出的模型在性能上优于最先进的方法，并有效提高了LLMs在突发事件谣言检测中的性能。
英文摘要：
The rapid spread of rumors on social media platforms during breaking events severely hinders the dissemination of the truth. Previous studies reveal that the lack of annotated resources hinders the direct detection of unforeseen breaking events not covered in yesterday's news. Leveraging large language models (LLMs) for rumor detection holds significant promise. However, it is challenging for LLMs to provide comprehensive responses to complex or controversial issues due to limited diversity. In this work, we propose the Stance Separated Multi-Agent Debate (S2MAD) to address this issue. Specifically, we firstly introduce Stance Separation, categorizing comments as either supporting or opposing the original claim. Subsequently, claims are classified as subjective or objective, enabling agents to generate reasonable initial viewpoints with different prompt strategies for each type of claim. Debaters then follow specific instructions through multiple rounds of debate to reach a consensus. If a consensus is not reached, a judge agent evaluates the opinions and delivers a final verdict on the claim's veracity. Extensive experiments conducted on two real-world datasets demonstrate that our proposed model outperforms state-of-the-art methods in terms of performance and effectively improves the performance of LLMs in breaking event rumor detection.
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。
