Summary of Detect, Investigate, Judge and Determine: a Knowledge-guided Framework For Few-shot Fake News Detection, by Ye Liu et al.
Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection
by Ye Liu, Jiajun Zhu, Xukai Liu, Haoyu Tang, Yanghai Zhang, Kai Zhang, Xiaofang Zhou, Enhong Chen
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers introduce Few-Shot Fake News Detection (FS-FND), an approach to distinguish inaccurate news from real ones in extremely low-resource scenarios. The task is crucial due to the widespread dissemination and harmful impact of fake news on social media. Large Language Models (LLMs) have shown competitive performance with their rich prior knowledge and excellent in-context learning abilities, but existing methods face limitations like Understanding Ambiguity and Information Scarcity. To address these shortcomings, the authors propose a Dual-perspective Knowledge-guided Fake News Detection (DKFND) model that enhances LLMs from both inside and outside perspectives. The DKFND model consists of four modules: Detection, Investigation, Judge, and Determination. It first identifies knowledge concepts through detection, then retrieves valuable information using investigation, evaluates relevance and confidence with the judge, and derives final predictions with determination. Experimental results on two public datasets demonstrate the effectiveness of the proposed method, particularly in low-resource settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news is a big problem on social media! This paper introduces a new way to detect fake news when we have very little information. It’s called Few-Shot Fake News Detection (FS-FND). Large Language Models (LLMs) are good at detecting fake news, but they need a lot of training data. The proposed method, DKFND, uses four modules to enhance LLMs and improve detection in low-resource scenarios. |
Keywords
» Artificial intelligence » Few shot
