AIBullisharXiv – CS AI · 7h ago7/10
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Learning Uncertainty from Sequential Internal Dispersion in Large Language Models
Researchers introduce Sequential Internal Variance Representation (SIVR), a novel supervised framework for detecting hallucinations in large language models by analyzing token-wise and layer-wise variance patterns in hidden states. The method demonstrates superior generalization compared to existing approaches while requiring smaller training datasets, potentially enabling practical deployment of hallucination detection systems.