Related papers: Detect, Investigate, Judge and Determine: A Novel LLM-based Framework for Few-shot Fake News Detection

Detect, Investigate, Judge and Determine: A Novel LLM-based Framework for Few-shot Fake News Detection

URL: http://arxiv.org/abs/2407.08952v1
Date: Fri, 12 Jul 2024 03:15:01 GMT
Title: Detect, Investigate, Judge and Determine: A Novel LLM-based Framework for Few-shot Fake News Detection
Authors: Ye Liu, Jiajun Zhu, Kai Zhang, Haoyu Tang, Yanghai Zhang, Xukai Liu, Qi Liu, Enhong Chen,
Abstract summary: Few-Shot Fake News Detection aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. We propose a Dual-perspective Augmented Fake News Detection model, designed to enhance Large Language Models.
Score: 47.01850264003063
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. Large Language Models (LLMs) have demonstrated competitive performance with the help of their rich prior knowledge and excellent in-context learning abilities. However, existing methods face significant limitations, such as the Understanding Ambiguity and Information Scarcity, which significantly undermine the potential of LLMs. To address these shortcomings, we propose a Dual-perspective Augmented Fake News Detection (DAFND) model, designed to enhance LLMs from both inside and outside perspectives. Specifically, DAFND first identifies the keywords of each news article through a Detection Module. Subsequently, DAFND creatively designs an Investigation Module to retrieve inside and outside valuable information concerning to the current news, followed by another Judge Module to derive its respective two prediction results. Finally, a Determination Module further integrates these two predictions and derives the final result. Extensive experiments on two publicly available datasets show the efficacy of our proposed method, particularly in low-resource settings.

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