The Alignment Paradox: How Post-Training Amplifies Confident Hallucinations in Language Models
Large language models (LLMs) can produce factually incorrect answers with high confidence, undermining their reliability and limiting the effectiveness of uncertainty-based error detection. While prior research attributes confident hallucinations to factors such as missing knowledge in training data, reasoning errors,...
Qing-Jia Huang, Ya-Kai Li, Jian-Guo Wu et al.
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