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School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China
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Stance detection in TikTok political discourse reveals striking differences in user engagement and opinion dynamics across major political figures.
BioStance reveals that nuanced bioethical discussions on social media can be effectively modeled with high reliability, challenging the limitations of existing stance detection datasets.
LLMs exhibit a surprising phase transition in error patterns as semantic complexity increases, challenging conventional approaches to stance detection.
ARMOR-MAD achieves up to 96.5% accuracy in multi-agent debate tasks by dynamically routing debate processes, showcasing the power of adaptive computation in large language models.
