Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation

arXiv:2601.06600v4 Announce Type: replace Abstract: Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language Models (MLLMs) have demonstrated impressive reasoning capabilities, their robustness against misinformation entangled with cognitive biases remains under-explored. In this paper, we introduce a comprehensive evaluation framework using a high-quality, manually annotated dataset of 200 short videos spanning four health domains. This dataset provides fine-grained annot

Source: arXiv cs.CL — read the full report at the original publisher.

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