LLMs systematically inflate Republican positions and deflate Democratic positions relative to human survey responses, consistent with representativeness heuristics.
Explore the Potential of LLMs in Misinformation Detection: An Empirical Study
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
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.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes
LLMs systematically inflate Republican positions and deflate Democratic positions relative to human survey responses, consistent with representativeness heuristics.