Summary of The Future Of Combating Rumors? Retrieval, Discrimination, and Generation, by Junhao Xu et al.
The Future of Combating Rumors? Retrieval, Discrimination, and Generation
by Junhao Xu, Longdi Xian, Zening Liu, Mingliang Chen, Qiuyang Yin, Fenghua Song
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the issue of artificial intelligence-generated content (AIGC) spreading misinformation online, threatening societal, economic, and political stability. The authors propose a comprehensive debunking process that not only detects rumors but also provides explanatory generated content to refute their authenticity. The process involves an Expert-Citizen Collective Wisdom module for assessing credibility and a retrieval module for retrieving relevant information from a real-time updated database. By leveraging prompt engineering techniques, the authors integrate the results into a Large Language Model (LLM), achieving satisfactory performance while reducing computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The AI-generated content problem is affecting democracy by spreading misinformation online. Current rumor detection efforts are limited to classification tasks and don’t address the issue effectively. This paper proposes a new approach that not only detects rumors but also explains why they’re false. The method involves an expert-citizen collective wisdom module for checking credibility and a retrieval system for finding relevant information. By using AI in a clever way, the authors make their model more efficient and cost-effective. |
Keywords
» Artificial intelligence » Classification » Large language model » Prompt
