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

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arxiv 2311.12699 v2 pith:PIGZXVTA submitted 2023-11-21 cs.CL cs.AIcs.CY

classification cs.CLcs.AIcs.CY
keywords misinformationdetectionllmsempiricalmodelspotentialabilitycapabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes

    cs.CL 2025-01 conditional novelty 6.0 of 10

    LLMs systematically inflate Republican positions and deflate Democratic positions relative to human survey responses, consistent with representativeness heuristics.

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