Explore the Potential of LLMs in Misinformation Detection: An Empirical Study

Mengyang Chen, Lingwei Wei, Han Cao, Wei Zhou, Songlin Hu

Keywords: Large Language Models, Misinformation Detection, Empirical Study

Abstract: This paper presents a comprehensive empirical study to explore the ability of Large Language Models (LLMs) in misinformation detection. Our empirical studies on eight misinformation detection datasets show that LLM-based detectors can achieve comparable performance in text-based misinformation detection but exhibit notably constrained capabilities in comprehending propagation structure compared to existing models in propagation-based misinformation detection. Our experiments further demonstrate that LLMs exhibit great potential to enhance existing misinformation detection models. These findings highlight the potential ability of LLMs to detect misinformation.

Submission Number: 23