Mitigating Hallucinations in Large Language Models Via Decoder Layer Skipping
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 introduce \textbf{DeLask} (\textbf{De}coder \textbf{La}yer \textbf{Sk}ipping), a novel decoding framework that dynamically skips layers prone to producing hallucinations. DeLask leverages the theoretical insight that the forward computation of an $L$-layer Transformer is conditionally equivalent to $...
| Authors | Hanze Li, Jinhao You, Yichen Guo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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