Summary of Veract Scan: Retrieval-augmented Fake News Detection with Justifiable Reasoning, by Cheng Niu et al.
VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning
by Cheng Niu, Yang Guan, Yuanhao Wu, Juno Zhu, Juntong Song, Randy Zhong, Kaihua Zhu, Siliang Xu, Shizhe Diao, Tong Zhang
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 The paper introduces VeraCT Scan, a system that detects fake news by extracting core facts from a piece of news and searching for corroborating or conflicting reports online. The system leverages sources’ credibility to verify information and provides transparent evidence and reasoning to support its conclusions. This approach achieves state-of-the-art accuracy in fake news detection using GPT-4 Turbo and Llama-2 13B, fine-tuned for news content understanding, verification, and reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to figure out what’s true or false about a news story. It can be really hard! That’s because there’s a lot of fake news out there that tries to trick us. This paper introduces a new way to detect fake news by looking at the facts in a story and comparing them to other reports online. The system also checks the credibility of the sources reporting the news. By doing this, it can tell us what’s true and what’s not. This approach is really good at getting things right! |
Keywords
» Artificial intelligence » Gpt » Llama
