Mitigating Hallucinations in Large Language Models Via Decoder Layer Skipping
A new paper on arXiv (2606.00819) presents DeLask (Decoder Layer Skipping), a framework to mitigate hallucinations in large language models. The authors conducted a layer-wise analysis of the decoding process and found that hallucinations tend to originate from deeper decoder layers. DeLask leverages the insight that forward computation in an L-layer Transformer is conditionally equivalent to L steps of gradient descent. It computes a 'driftance value' using cosine similarity between gradients from consecutive decoder steps to identify problematic layers where the descent direction reverses. Instead of discarding these layers, DeLask partially aggregates their hidden states with preceding layers, preserving consistency while suppressing erroneous signals. Experiments across diverse LLMs demonstrate reduced hallucination rates without requiring model retraining or additional data. This approach offers a practical, inference-time solution for improving factual accuracy, which is critical for applications like customer support, code generation, and content creation where reliability is paramount.
Reduces hallucinations in LLMs without retraining, enabling safer deployment in production.