Automatic Layer Selection for Hallucination Detection

ArXi:2605.26366v1 Announce Type: new Recent studies on hallucination detection have shown that hallucination-related signals are 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.