Explore the Potential of LLMs in Misinformation Detection: An Empirical Study
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910762830659584 |
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| author | Chen, Mengyang Wei, Lingwei Cao, Han Zhou, Wei Hu, Songlin |
| author_facet | Chen, Mengyang Wei, Lingwei Cao, Han Zhou, Wei Hu, Songlin |
| contents | Large Language Models (LLMs) have garnered significant attention for their powerful ability in natural language understanding and reasoning. In this paper, we present a comprehensive empirical study to explore the performance of LLMs on misinformation detection tasks. This study stands as the pioneering investigation into the understanding capabilities of multiple LLMs regarding both content and propagation across social media platforms. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12699 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Explore the Potential of LLMs in Misinformation Detection: An Empirical Study Chen, Mengyang Wei, Lingwei Cao, Han Zhou, Wei Hu, Songlin Computation and Language Artificial Intelligence Computers and Society Large Language Models (LLMs) have garnered significant attention for their powerful ability in natural language understanding and reasoning. In this paper, we present a comprehensive empirical study to explore the performance of LLMs on misinformation detection tasks. This study stands as the pioneering investigation into the understanding capabilities of multiple LLMs regarding both content and propagation across social media platforms. 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. |
| title | Explore the Potential of LLMs in Misinformation Detection: An Empirical Study |
| topic | Computation and Language Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2311.12699 |