Humans cannot reliably distinguish LLM-generated news from human-written news across multiple models, with domain expertise providing only modest help and fatigue reducing accuracy over time.
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4 Pith papers cite this work. Polarity classification is still indexing.
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IDO uses channel-wise reweighting, Gaussian modeling of factual uncertainty, and incongruity contrastive learning to achieve SOTA multimodal fake news detection.
Gini index of BERTopic topic distributions on COVID-19 Reddit data shows significant global correlation with fake news fraction but ambiguous results at community level.
A survey reviewing benchmark data contamination in LLMs, its impact on evaluation, and alternative assessment approaches.
citing papers explorer
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Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News
Humans cannot reliably distinguish LLM-generated news from human-written news across multiple models, with domain expertise providing only modest help and fatigue reducing accuracy over time.
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IDO: Incongruity-aware Distribution Optimization for Multimodal Fake News Detection
IDO uses channel-wise reweighting, Gaussian modeling of factual uncertainty, and incongruity contrastive learning to achieve SOTA multimodal fake news detection.
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Quantifying correlations between information overload and fake news during COVID-19 pandemic: a Reddit study with BERT model approach
Gini index of BERTopic topic distributions on COVID-19 Reddit data shows significant global correlation with fake news fraction but ambiguous results at community level.