Automatic Layer Selection for Hallucination Detection
Recent studies on hallucination detection have shown that hallucination-related signals are more strongly encoded in intermediate layers than in the final layer of large language models (LLMs). Although a growing body of work has sought to exploit this property for hallucination detection, how to automate the selection of high-performing layers remains underexplored, and principled methods for this purpose are still lacking. To address this gap, we first propose several hypotheses for why such signals emerge in intermediate layers and evaluate corresponding criteria for automatic layer selection across diverse LLM architectures, scales, and tasks, covering both question answering and summ...
| Authors | Xinpeng Wang, William X. Cao, Andrew Gordon Wilson et al. |
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| Primary category | cs.AI |
| Published | 25 May 2026 |
| Last updated at source | 2 June 2026 |
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