Original title: Can Large Language Models Understand Content and Propagation for Misinformation Detection: An Empirical Study
Authors: Mengyang Chen, Lingwei Wei, Han Cao, Wei Zhou, Songlin Hu
In the world of language models, these powerful tools are put to the test: spotting misinformation. This study delves deep, exploring how well Large Language Models (LLMs) understand and tackle misinformation across social media. They threw these models into the ring with five datasets designed to trip up misinformation detectors. The results? LLMs, when prompted differently, nail down text-based misinformation detection like pros. But when it comes to understanding how misinformation spreads, they stumble compared to other existing models. The researchers didn’t stop there. They crafted new strategies to give these LLMs a boost. By fine-tuning their learning approaches, these models got sharper at spotting both the content and how misinformation moves around. The experiments proved it: with these tweaks, LLMs showed serious potential in the misinformation detective game. It’s like teaching them to not just read the story, but also understand how it travels through the grapevine.
Original article: https://arxiv.org/abs/2311.12699