Mitigating Hallucinations in Large Language Models Via Decoder Layer Skipping

ArXi:2606.00819v1 Announce Type: new Large Language Models (LLMs) have achieved strong performance across diverse natural language tasks, yet their outputs often suffer from hallucinations -- content that is misaligned with factual information. In this work, we conduct a comprehensive layer-wise analysis of the decoding process and reveal that hallucinations tend to originate from deeper decoder layers. To address this issue, we